Welcome to Issue 60 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. Open Science Summer School 2026

The LMU Open Science Center organises a hybrid Open Science Summer School, happening Sept. 7-11, to allow early-career researchers to make their research more reliable, computationally reproducible, and credible in the eyes of their peers, the public, and funding agencies. The summer school consists of workshops for selected applicants and public lectures. Anyone can register to attend one, multiple, or all public lectures online. Lecture topics include “A Guerilla approach to scientific workflows”, “Readable Code”, “Data Sharing”, and more.

2. ManyScale

Psychology measurement lacks standardization — and standardizing measures starts with documenting them. Cataloguing the field’s self-report measures is a crucial step toward reducing measurement proliferation, improving consistency across studies, and making our science more cumulative. ManyScale is a network of repositories for self-report measures, each covering one subfield within psychology. Building repositories by subfield keeps each one manageable in scope — maintained by researchers embedded in that community who know and use the measures firsthand. This free and open-source project is looking for collaborators who are interested in leading repositories in their respective subfields. They can give you the tools to help you make a ManyScale for your research area. For a plug-and-play option, they can create the repository on their server (you just add and maintain the measures). Alternatively, they can share the code so you can create your own self-hosted version. If you are interested in hosting a repository for your research area, please get in touch.

3. Open Research Won’t Slow Your Career Down – It’s the Smartest Way to Build it

Is open research a virtue that early career researchers can’t afford? This article shares how to make open practices work for you from your very first project – without adding hours to your week.

4. Radical Reproducibility, Real Constraints: An Autoethnography of Open and Transparent Research From the Inside

This paper presents a three-year longitudinal autoethnographic study of the TIER2 project, an international, interdisciplinary consortium committed to “radical reproducibility” through open research practices. Using an auto-ethnographic approach, the authors investigated how epistemic diversity, disciplinary norms, and institutional cultures shape reproducibility in practice.

5. ELNS IN CHEMISTRY - From Implementation to Sustainable Stewardship

Electronic Laboratory Notebooks (ELNs) are becoming central to chemistry research, but successful adoption depends on far more than selecting a software platform. ELNs affect how researchers record experiments, attach data, review work, manage knowledge and preserve the scientific record. Poorly planned implementation can create extra administrative burden, fragmented workflows and long-term risks around access, ownership and sustainability. This whitepaper provides practical guidance for chemistry departments considering implementing or maintaining an ELN. The whitepaper sets out key criteria for choosing an ELN, including compliance, data integrity, functionality, usability, accessibility, interoperability, scalability and total cost of ownership. It then examines implementation in practice, emphasising realistic trials, clear ownership, process mapping, sensible metadata, laboratory hardware, training and continued support. It also considers change management, including governance, stakeholder engagement, communication and embedding ELN use into everyday research culture.

6. Academics ‘See Research Security as Bureaucratic’, UK Survey Finds

This article covers a 2025 UK government survey showing academics are far less engaged with and confident about research security than professional services staff or senior leaders, often viewing it as irrelevant, overly bureaucratic or burdensome. It also notes confidence gaps are especially stark around unfunded research collaborations, even as senior leadership engagement grows.

7. Interactive Git Exercises for Quarto Documents

As Ryan Johnson says, “Teaching Git, especially through the terminal, is hard. Blank prompt, cryptic errors, and one wrong command away from a mess.” That’s why he built a Quarto extension that puts a real Git repository right in the browser. Learners type real git commands and watch a working-directory/staging/repository diagram, a live diff of what changed, and a live commit graph update as they go. Exercises can also add a mock remote: a second repository inside the page that learners push to, fetch and pull from, drawn as its own commit graph, including realistic rejected pushes when the remote is ahead. Nothing is installed, nothing is uploaded, nothing is written to disk. Both repositories live in memory and disappear on reload. Read the full documentation here.

Oldies but Goodies

Older resources that are still helpful

1. HEAL Data Packaging Tool

Compiling and accurately documenting the aspects of your study's data and supporting documents in a way that will be understandable and usable for future researchers can be a difficult task. This tool was developed to help ease the burden of understanding and effectively fulfilling the NIH HEAL Initiative data sharing requirements. This tool provides you with easy-to-fill-out forms and step-by-step guidance that will help you to document and annotate your study’s experiments, results, and resources to maximize your study’s findability and replicability by future researchers. When you fill out these forms, the tool will package up the information into different trackers, which you will then deposit into the repository you choose, along with your other data package components, as supporting documentation. The tool and the resulting trackers will provide systematic annotation of your data package.

2. Ten Simple Rules to Enable Multi-site Collaborations through Data Sharing

Open access, open data, and software are critical for advancing science and enabling collaboration across multiple institutions and throughout the world. Despite near universal recognition of its importance, major barriers still exist to sharing raw data, software, and research products throughout the scientific community. Many of these barriers vary by specialty, increasing the difficulties for interdisciplinary and/or translational researchers to engage in collaborative research. Multi-site collaborations are vital for increasing both the impact and the generalizability of research results. However, they often present unique data sharing challenges. The authors discuss enabling multi-site collaborations through enhanced data sharing in this set of Ten Simple Rules.

3. FAIRSharing.org

A curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies. Guides consumers to discover, select and use these resources with confidence. Helps producers to make their resources more visible, more widely adopted and cited. Provides humans and tools with access to trustworthy content to enable data management tasks.

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.

• University of Essex, UK Data Archive - Head of Data Management and Discovery

• Penn State - Children’s Eating Behavior Lab - Research Data Management Specialist

• Wits Health Consortium - Data Manager

• GSK - SPQS Data Steward

• Harvard Kennedy School - Research Data Specialist

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.

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.

Buy Me A Coffee

Welcome to Issue 59 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. Enacting Open Science Values in Qualitative Leadership Research: Existing Practices and Future Directions

The open science movement has gained considerable momentum in advancing the transparency, credibility, and rigor of scientific research across disciplines. Within this broader landscape, leadership scholars have played a prominent role in developing formal guidance to strengthen open science practices in organizational research. To date, most recommendations have focused on quantitative research rooted in post-positivist traditions. However, qualitative scholars working from interpretivist perspectives share core values such as openness, integrity, and reproducibility with open science and enact them through practices grounded in inductive, context-sensitive inquiry. Bringing these two conversations into closer dialogue offers meaningful opportunities for both domains. Leadership research provides a particularly generative context for this exchange, as qualitative methods have influenced the field by illuminating the socially constructed and value-laden nature of leading. Drawing on these contributions, the authors present a set of 12 practices through which interpretivist qualitative researchers have historically embodied open science values in leadership research, followed by six additional practices that can further enhance the openness, integrity, and reproducibility of interpretivist scholarship. By articulating how open science principles can be enacted in qualitative paradigms, the authors show that leadership scholars are well-positioned to extend the open science conversation across methodologies and communities of practice.

2. ASCOLLECTED Platform

AsCollected, from the Wharton Credibility Lab at the University of Pennsylvania, is a platform for documenting results provenance in scientific publications; when and how data were obtained and who cleaned & analyzed them. Every AsCollected includes two tables; Table 1 shows author roles, Table 2 data provenance.

3. Webinar: Navigating the New NIH Data Management and Sharing Plan Format: Guidance, Resources, and Practical Perspectives

The NIH’s revised Data Management and Sharing (DMS) Plan format, which took effect in May 2026, changed how researchers describe their anticipated data and other key aspects of data sharing. Subsequently, NIH released NOT-OD-26-100 (Implementation Update: NIH Data Management and Sharing Plan Requirements) effective October 1, 2026, requiring all existing awards and applications subject to the NIH Data Management and Sharing (DMS) Policy to transition to and align with the updated 2026 DMS Plan format. These changes also have significant implications for librarians and research data professionals who support investigators in developing and implementing DMS Plans. To help data management services respond to these changes, an RDAP (Research Data Access & Preservation) Association working group updated a practical toolkit of resources and guidance. In this webinar on September 2nd, four members of the working group will provide an overview of the new NIH DMS Plan format, highlight major changes, and demonstrate how the toolkit can support both librarian and researcher needs. Drawing on their experiences supporting researchers through the transition, the presenters will address common challenges and provide strategies to adapt to evolving requirements.

4. SIACast Podcast

SIAcast tunes you in to the discussions shaping research integrity and meta-research. In the most recent episode, Reproducbility Networks and the Future of Research, the hosts dig into a growing crisis in research integrity, from AI-generated “papers” and hallucinated citations to peer-review overload and stealth edits in published work. With real-world examples (including a famously impossible lab rat image that passed peer review), the episode pulls back the curtain on how flawed science can slip through and why it’s happening more often. But this isn’t a doom-and-gloom story. Featuring insights from research integrity experts Joanna Diong and René Aquarius, the conversation explores the human, institutional, and systemic forces shaping modern science. From perverse incentives and publication pressure to grassroots sleuthing communities fighting back, this episode asks a critical question: can we still trust science, and if so, what will it take to protect that trust?

5. Data Quality in Online Crowdsourced Surveys: Methodological Challenges and Analytic Insights in Survey Research

Pay-for data collection platforms are increasingly popular in health sciences, due to the ease of use, accessibility, and speed of data collection. However, data quality among platforms may differ. The purpose of this study was to compare data quality from three commonly used, pay-for data collection crowdsourcing platforms, using predefined quality control measures. Data were collected using three major platforms, Qualtrics (N = 705), SurveyMonkey (N = 576), and Amazon Mechanical Turk (N = 1,034), for the methodological purpose of assessing data quality for a scale used by public health practitioners to assess ankle activity level. Data were collected using attempted quota sampling for gender, age, ethnicity and region, for representativeness of the general US population. Twenty quality checks in five categories were utilized to identify poor-quality data: attention verification (e.g. ‘please select response B’); demographic verification (e.g. state and zip code match); illogical responses (e.g. respondents separately reporting higher difficulty walking up one flight of stairs than walking up four flights); honesty/reliability verification (e.g. male reported to be pregnant); and assessment of open-ended questions (rejected based on similarity of content or nonsensical responses). Of the 2,315 completed responses received from the three different platforms, 947 (40.91%) passed our quality checks and were considered good-quality responses. SurveyMonkey yielded the highest proportion of good-quality data (70.5%), followed by Qualtrics (61.8%) and Amazon Mechanical Turk (10.2%); however, open-ended responses could not be included in SurveyMonkey due to their 80-item limit, which may have inflated its proportion of good-quality responses. This study demonstrates that data quality issues may differ across platforms and a variety of quality control measures should be implemented for researchers utilizing pay-for data collection services from crowdsourcing platforms.

6. For a Decade CRediT has Quietly Reshaped Academic Authorship – What Comes Next?

The Contributor Roles Taxonomy, or CrediT, has over the past years clarified how different hands lead to the creation of research papers. Taking stock of the past decade, Andrea Chiarelli, Lucia Loffreda, Liz Allen, Mohammad Hosseini, Sandra Bendiscioli and Holly Ranger consider CrediT’s success and the challenges it faces if it is to become more embedded in funding and research assessment processes.

7. Unit Testing 101

In this tutorial Jacci Ziebert explains why and when to write unit tests for R functions, then walks through building tests (using the testthat package), first for a simple function and then for a more complex function to show good test coverage. It concludes with best practices (isolated tests, testing edge cases, breaking code into small functions) and practical tips, including using LLMs to help draft initial unit tests.

Oldies but Goodies

Older resources that are still helpful

1. Guidebook - Depositing Restricted Access in the DANS Data Stations

This guidebook is written for anyone who considers depositing data in one of the DANS Data Stations, it provides information that is useful for anyone looking to deposit restricted access data. The guidebook outlines the possibilities that DANS offers to deposit data under open and restricted access. It provides guidance for depositors who chooses to publish data with restricted access, explaining what to consider and how to create a data access protocol.

2. Tidy Data

A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.

3. The Research Data Life Cycle, Legacy Data, and Dilemmas in Research Data Management

This paper presents findings from an interview study of research data managers in academic data archives. The study examined policies and professional autonomy with a focus on dilemmas encountered in everyday work by data managers. The authors found that dilemmas arose at every stage of the research data lifecycle, and legacy data presents particularly vexing challenges. The iFields' emphasis on knowledge organization and representation provides insight into how data, used by scientists, are used to create knowledge. The iFields' disciplinary emphasis also encompasses the sociotechnical complexity of dilemmas that the authors found arise in research data management. Therefore, the authors posit that iSchools are positioned to contribute to data science education by teaching about ethics and infrastructure used to collect, organize, and disseminate data through problem-based learning.

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.

• Binghamton University - Libraries Postdoctoral Associate in Open Data

• Cranfield University - Trusted Research Manager

• Massachusetts Institute of Technology - Research Data Librarian

• Boston University Medical Campus - Senior FHS Data Manager/Analyst

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.

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.

Buy Me A Coffee

Welcome to Issue 58 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. OSF is Changing

Last week the Center for Open Science made a huge announcement regarding the OSF platform. Due to funding challenges for open research infrastructure, the OSF Projects workflow – the part of the OSF used for project collaboration, file management, and storage – is being phased out as an active workspace. This change does not affect persistence and accessibility of existing public projects. You can learn more about the change in this post, and in an upcoming webinar on August 27th at 10am central time. In the webinar the COS will share why these changes are happening, review the key dates and what they mean for users, clarify what will and will not change, and share practical guidance for preparing for the transition. Alongside this announcement, the OSF released a post titled “Where should I share my Data, Materials, Code now?” and Daniel Lakens released a post, "Which Data Repository Should You Use?”, to help researchers consider alternatives to OSF for storing data.

2. Data Do’s and Don’ts: A Primer on Indigenous Data Sovereignty

This guidebook was developed by the CBIKS (Center for Braiding Indigenous Knowledges and Science) Data Sovereignty Co-Lab for use by the CBIKS team in managing data across projects. This tool is useful to researchers in many disciplines, as it outlines data types, legal and governance foundations, provenance, collection & consent, storage infrastructure & access, use & reuse, attribution & accountability, and common misconceptions.

3. Agentic Workflows with Claude Code

This site contains the recording and materials from a recent free workshop on agentic workflows using Claude Code. The workshop covered Claude Code basics (getting setup, agent basics, and Claude Code essentials), skill usage and design, and working safely with data and AI (using GitHub, protecting sensitive data).

4. PondPilot

PondPilot is a free, open-source data tool that runs entirely in your browser, letting you open and review proprietary statistical file formats, such as Stata (.dta), SAS (.sas7bdat/.xpt), and SPSS (.sav), without needing licensed software, and without uploading your data anywhere. Beyond just viewing these files, it gives you a full SQL engine to query, filter, and reshape the data, then export the results to common formats like CSV, JSON, or Excel. It also handles straightforward format conversions, like turning a CSV into a Parquet file, allowing you to fix the types in SQL before you export.

5. Who Will Keep Research Data Infrastructure Open and Running?

This article argues that open research data infrastructure (repositories, preservation systems, and data platforms essential to science) is chronically underfunded and increasingly failing, with nearly 200 repositories shut down in the past 25 years because funding flows to researchers who use these tools rather than to the infrastructure itself. The authors call for structural fixes like dedicated funding line items, sustained philanthropic support, and diversified funding sources to prevent further collapse of systems that underpin scientific progress.

6. Guide - Versioning: When is it a release?

This guide explains three main software versioning systems—semantic versioning (MAJOR.MINOR.PATCH), date-based versioning (like CalVer), and named releases (like macOS’s big cats), and helps readers choose based on their release habits, whether they favor frequent patches, periodic releases, or distinct named milestones. The article’s key advice is that whichever system you pick, consistency matters most: use it the same way every time so both developers and users can clearly track and communicate about specific builds.

7. Data Management Workflow for FAIR Sharing of Bioimaging Datasets in Plasma Medicine

Bioimaging experiments in plasma medicine generate datasets that extend beyond conventional imaging studies by combining microscopy data with heterogeneous metadata from biological experiments and gas plasma treatments. These data are often distributed across multiple systems, making it difficult to maintain links between imaging data, plasma treatment conditions, biological metadata, and microscopy acquisition settings. This fragmentation limits data sharing and repository deposits. To address this challenge, the authors present a data management workflow for FAIR sharing of bioimaging datasets in plasma medicine. The workflow is implemented as an open-source Jupyter Notebook and connects the established research data management tools Open Microscopy Environment Remote Objects (OMERO) for image data management, eLabFTW as an electronic laboratory notebook for experimental documentation, Adamant as a schema-based metadata acquisition tool, and Micro-Meta App for the standardized capture of microscopy settings. The workflow guides researchers through the data management process, supporting the adoption of the FAIR data principles for the sharing and reuse of imaging datasets.

Oldies but Goodies

Older resources that are still helpful

1. National Czech Programme Beginner’s Guide for Data Stewards

This guide is a practical onboarding roadmap for new research data stewards (DS), walking readers through four stages: getting familiar with open science/RDM basics and tools, mapping out support and stakeholders within an institution, deepening that institutional knowledge (repositories, standards, services), and actively supporting researchers with grant applications and Data Management Plans. It emphasizes gradual progress, building relationships with researchers and colleagues, and staying current with open science developments, and includes appendices with resource links, a researcher interview template, and a training-design worksheet.

2. Using a Data Dictionary As Your Roadmap to Quality Data

A data dictionary, a rectangular format collection of names, definitions, and attributes about variables in a dataset, is arguably the single most important piece of documentation you will create in your research study. While a data dictionary, sometimes also called a codebook or variable information log, is often used as a tool to help you and others interpret your data at the end of your project, it is actually even more powerful if created before you ever collect a single piece of data, serving as a roadmap as you design your data collection tools and clean your data, in order to ultimately get to where you want to go.

3. A Template README for Social Science Replication Packages

The template README provided on this website is in a form that follows best practices as defined by a number of data editors at social science journals. A full list of endorsers is listed in Endorsers

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.

• Wellcome Trust - Open Research Specialist

• NHS Scotland - Data Manager – West of Scotland Safe Haven

• University of Kentucky - Research Data Analysis Librarian

• Canberra Health Services - Research Support Officer

• The University of Utah - Technical Business Analyst I

• Cradle-to-Career - Research Data Specialist III

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.

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.

Buy Me A Coffee

Welcome to Issue 57 of the RDM Weekly Newsletter!

Surprise! I had planned to take this week off from the newsletter but the resources started piling up so I decided to go ahead and send one out!

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. Analyst Notes: Five Foundational Concepts for Understanding Data

“Let the data speak for itself” is a common refrain in the world of data and visualizations. But charts can have characteristics that can affect how they are interpreted. At USAFacts, they share the data and let you draw your own conclusions. To help put that data in context, Jackie J. N. identified five foundational concepts that can shape how data is collected, reported, and interpreted. Understanding them can help you get more out of the charts and datasets you see here, and elsewhere.

2. EPFL RDM Training Materials available as Open Educational Resources

These materials complement the training courses offered by the EPFL (Ecole Polytechnique Fédérale de Lausanne) Library. They can be used to prepare for a course, review key concepts covered during training sessions, or repurpose educational content for other teaching and training contexts. The resources currently available include 5 Modules covering ABC of Research Data Management, Data Management Planning and Funding, Data Organization and Storage Solutions, Ethics in Research Data Management: Anonymization and Best Practices, and Data/Code Publication.

3. TOP 2025: An Update to the Transparency and Openness Promotion Guidelines

Transparency and openness are core scientific values that enable independent verification of reported research results. The Transparency and Openness Promotion (TOP) Guidelines, first published in 2015 (TOP 2015), provided a flexible framework for journals to design and implement publication standards promoting these values. The TOP Advisory Board undertook a comprehensive update of TOP 2015 that went into effect in 2025 (TOP 2025). The update introduces an explicit conceptual framework clarifying its primary objective: improving the verifiability of empirical research claims. Standards are reorganized into three categories (Research Practices, Verification Practices, and Verification Studies) that resolve conceptual and structural limitations of TOP 2015. Research Practices include seven discrete practices implemented in three ways: Disclosed, Shared and Cited, and Certified. Verification Practices introduce standards for computational reproducibility and results transparency, while Verification Studies enumerate empirical research designs and publication formats that support verification of results. TOP 2025 retains the core strengths of TOP 2015 while addressing feedback from a decade of implementation and advances in open science. Rather than a universal mandate, it provides a shared, adaptable framework to coordinate the design, implementation, and communication of open policies across interest-holders.

4. Why ‘Provable Data Erasure’ Is Really Hard

The article argues that modern data ecosystems make it extremely difficult for organizations to verify that data has been permanently erased. Distributed systems, third-party services, backups, and incomplete visibility into where data resides all contribute to this challenge.

5. The Metadata Ecosystem and AI: Enabling FAIR and AI-Ready Data

Reproducibility is a foundational tenet of science. As artificial intelligence (AI) becomes increasingly embedded across science, the need to accurately document the provenance, structure, and behavior of training data, models, and workflows grows correspondingly. Metadata, understood as explicit and structured knowledge about data and related entities, is a critical yet often underexamined component of AI systems that helps address this need. High-quality metadata describing datasets, models, and workflows supports the FAIR (Findable, Accessible, Interoperable, Reusable) principles, strengthens reproducibility, and enables evaluation of AI-readiness by making data and models interpretable, traceable, and structurally consistent. Despite its central importance, sustained discussion of metadata as a core component of AI infrastructure remains limited. This article examines the role of metadata in advancing AI-enabled research, focusing on the design, implementation, and operationalization of metadata standards within an evolving metadata ecosystem.

6. RStudio User Guide

Version 43 of this documentation helps a user understand the core workflows in RStudio. It provides information for navigating the user interface and appearance, managing R and R packages, working with Python, authoring code or text editing, debugging and diagnostics, using the Visual Editor for Quarto and RMarkdown, reading in data from local and remote sources, and integrations with version control systems and the terminal.

7. Ten Quick Tips to SNIFF out Sustainable and Secure Scientific Software

Modern computational biology depends heavily on open-source software tools, analysis pipelines, and containerized workflows developed and shared by the research community. While there is extensive guidance (including Quick Tips and Simple Rules articles) on how to build robust and sustainable scientific software, far less has been written for researchers in the role of software users evaluating whether an existing tool is reliable, secure, and sustainable enough for their work. Here the authors present ten quick tips to help researchers critically assess the tools they adopt. The tips are organized around a framework that centers on key evaluation features: source, network, interaction, fit, and fragility (SNIFF). These dimensions prompt researchers to consider who maintains a tool and why, whether it is embedded in a broader ecosystem, how actively its developers and users engage, whether it matches the intended use case and licensing requirements, and how robust its dependencies and security practices are. By applying these tips, researchers can make more informed decisions, reduce the risk of relying on abandoned or insecure software, and contribute to a more sustainable scientific software ecosystem.

Oldies but Goodies

Older resources that are still helpful

1. STROBE - Strengthening the Reporting of Observational Studies in Epidemiology

Incomplete and inadequate reporting of research hampers the assessment of the strengths and weaknesses of the studies reported in the medical literature. Readers need to know what was planned (and what was not), what was done, what was found, and what the results mean. Recommendations on the reporting of studies that are endorsed by leading medical journals can improve the quality of reporting. Observational research comprises several study designs and many topic areas. The authors aimed to establish a checklist of items that should be included in articles reporting such research – the STROBE Statement. They considered it reasonable to initially restrict the recommendations to the three main analytical designs that are used in observational research: cohort, case-control, and cross-sectional studies. The authors want to provide guidance on how to report observational research well. The recommendations are not prescriptions for designing or conducting studies. Also, the checklist is not an instrument to evaluate the quality of observational research.

2. Spreadsheet Munging Strategies

This open access book is about getting data out of spreadsheets, no matter how peculiar. The book is designed primarily for R users who have to extract data from spreadsheets and who are already familiar with the tidyverse. It has a cookbook structure, and can be used as a reference, but readers who begin in the middle might have to work backwards from time to time.

3. Excel Errors: The UK Government Has an Embarrassingly Long History of Spreadsheet Horror Stories

The article argues that the UK government's COVID-19 spreadsheet error was not an isolated mistake but part of a long history of costly spreadsheet failures caused by using spreadsheets for tasks they were never designed to handle. It contends that critical systems require the same engineering practices, testing, and governance as traditional software to reduce the risk of serious errors.

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.

• GESIS – Leibniz-Institute for the Social Sciences - Research Infrastructure Specialist (Postdoc) for Trusted Research Environments

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.

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.

Buy Me A Coffee

Welcome to Issue 56 of the RDM Weekly Newsletter!

Heads up, there will be no newsletter next week. RDM Weekly will return Tuesday August 18th!

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. Announcing the Coalition for Resilient Research Data Infrastructure (CRRDI) Strategic Plan

Since Fall 2025, the Center for Open Science (COS) has been working with a committee of experts in research data infrastructures to develop a strategic plan for the Coalition for Resilient Research Data Infrastructure (CRRDI) — a nascent cross-sectoral coalition working to build a more resilient ecosystem for federally funded research data. Recently they released that draft plan for community comment by September 30, 2026. You can also join them on Friday, August 7 at 2pm ET for a webinar launching the draft strategic plan.

2. Quantitative Research in Organizations Using Excel

This open access book is intended for working professionals interested in learning quantitative research methods in organizational contexts. It serves as an introductory book that provides a basic understanding and perspective for working professionals beginning quantitative research. While statistical concepts are less emphasized, their organizational application has been highlighted. The book is composed of 10 chapters that cover exploring research methods, collecting and preparing quantitative data, conducting ethical research, and more.

3. Reading the Chart Before You Clean It

Using this field guide created by Karin Camila Boynton in conjunction with Claude, begin to understand six dimensions of data quality. The guide includes descriptions, quizzes, and scenarios. After working through the guide, you can try out Part 2, Fixing What the Audit Found. Learn how missing values, duplicates, and entry errors get handled, then practice on a messy export.

4. How to Have Difficult Conversations: A Practical Guide for Academic and Practitioner Research Collaborations

This document from the MIT GOV/LAB is meant to highlight and provide guidance on how to have “difficult conversations” that often arise when academic researchers and practitioners decide to collaborate. The focus here on difficult conversations is intentional because the authors want to hone in on pivotal decision points and issues that are frequently overlooked, or brought up too late. The guide is structured in a series of questions meant to clarify priorities and spell out assumptions. Each section includes questions that both partners should consider together, with specific lists aimed at academics and practitioners. The guide also includes several helpful resources.

5. Guide to Data Tools Landscape for Developers

Written for developers and others new to modern data infrastructure, this article provides a high-level overview of how data moves from collection and storage to processing, analysis, and end-user applications. Along the way, it introduces the key roles, architectures, and tools that make up today's data ecosystem.

6. University of Cambridge Research Data Management Policy Framework

I always like sharing examples of universities that have developed institution-wide research data management frameworks. The purpose of this policy framework is to provide guidance to members of the research community at the University of Cambridge by defining their responsibilities in managing the research data they use and material they produce, securely, legally and ethically, throughout the research data lifecycle. This guidance facilitates the continued maintenance and preservation of research data, making them available to the widest possible audience for the highest possible impact, and in support of the University's mission and core values. It is intended to align with the FAIR and CARE principles. This policy framework applies to all members of staff and students and research-enabling staff (e.g. professional services, technical) at the University.

7. ByteSized dRTP: Secure Data Principles for Digital Research

For this fourth session in the ByteSized dRTP series from STEP UP, happening Wednesday August 6th 10:30am-12pm BST, attendees will be looking at security-by-design principles. As research becomes increasingly data-driven, understanding security is becoming a core professional skill. Attendees will explore how to practically apply security-by-design principles, proportionate to research environments, and how governance, engineering, and research teams can work together to manage risk while keeping pace with innovation. You can register here. Each ByteSized dRTP session also includes a companion podcast episode released through the Code for Thought podcast series.

Oldies but Goodies

Older resources that are still helpful

1. Guidelines for a Codebook

These guidelines, from the Institutes of Education Sciences, describe what a codebook is, why it's important, and what the contents should be. It also provides a brief example codebook.

2. NIST Research Data Framework (RDaF): Version 2.0

The NIST Research Data Framework (RDaF) is a multifaceted and customizable tool that aims to help shape the future of open data access and research data management (RDM). The RDaF will allow organizations and individual researchers to develop their own RDM strategy. The audience for the RDaF is the entire research data community in all disciplines—the biological, chemical, medical, social, and physical sciences and the humanities. The RDaF is applicable from the organization to the project level and encompasses a wide array of job roles involving RDM, from executives and chief data officers to publishers, funders, and researchers. The RDaF is a map of the research data space that uses a lifecycle approach with six stages to organize key information concerning RDM and research data dissemination. An interactive web application has been developed and released that provides an interface for all the components of the RDaF mentioned above and replicates this document. The web application is easy and intuitive to navigate and provides new functionality enabled by the interactive environment.

3. FAIR Data Sharing: The Roles of Common Data Elements and Harmonization

The value of robust and responsible data sharing in clinical research and healthcare is recognized by patients, patient advocacy groups, researchers, journal editors, and the healthcare industry globally. Privacy and security concerns acknowledged, the act of exchanging data (interoperability) along with its meaning (semantic interoperability) across studies and between partners has been difficult, if not elusive. One effort to standardize data collection has been through common data elements (CDEs). CDEs are data collection units comprising one or more questions together with a set of valid values. Some CDEs contain standardized terminology concepts that define the meaning of the data, and others include links to unique terminology concept identifiers and unique identifiers for each CDE; however, usually CDEs are defined for specific projects or collaborations and lack traceable or machine readable semantics. While the name implies that these are ‘common’, this has not necessarily been a requirement, and many CDEs have not been commonly used. This manuscript explores reasons for the disappointingly low adoption of CDEs and the inability of CDEs or other clinical research standards to broadly solve the interoperability and data sharing problems. Recommendations are offered for rectifying this situation to enable responsible data sharing that will help in adherence to FAIR principles and the realization of Learning Health Systems for the sake of all of us as patients.

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 a Start - Salesforce and Data Systems Coordinator

• Carnegie Mellon University - Postdoctoral Associate in Meta-Research

• University of Alberta - Research Data Management Implementation Specialist

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.

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.

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Welcome to Issue 55 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. data.table, base, dplyr, pandas, and polars

This page presents a side-by-side comparison of common data manipulation operations in five idioms: data.table, base, dplyr, polars, and pandas. This allows you to compare syntax and understand how to accomplish tasks across these popular frameworks. This reference guide covers everything from basic filtering and sorting to advanced operations like joins and reshaping data. Many of these examples were originally crafted by Atrebas. They were then reorganized and augmented with base examples by a team of contributors.

2. A Methodological Metamorphosis: The Rapid Rise of Bayesian Inference and Open Science Practices in Psychology

The field of psychology is in the midst of a methodological revolution. To quantify the changes, the authors tracked the adoption of Bayesian inference and open science practices across 28,738 articles from six top-tier (15,634 articles) and six mid-tier (13,104 articles) psychology journals over a twenty-year period (2004–2024). For the top-tier journals, human (50%) and LLM-assisted (50%) full-text coding revealed that in 2024, about 1 in every 4 articles (26%) reported a Bayesian analysis, about 2 in every 5 articles (41%) included a preregistration, and about 3 in every 4 articles (73%) provided open data. For the mid-tier journals, LLM full-text coding showed that in 2024, about 1 in every 6 articles (16%) contained a Bayesian analysis, about 1 in every 5 articles (18%) was preregistered, and about 2 in every 5 articles (40%) openly shared data. From 2004 to 2012, these practices were virtually nonexistent. Application of a Bayesian logistic growth model suggests these practices will continue to rise in the future, although the extent of their growth remains uncertain. In addition, the authors’ review revealed that mixed-effects models have become more prevalent; software reporting has improved, with growing use of R alongside a relative decline of SPSS; and that estimation-focused reporting showed only modest gains.

3. Testing Incentive Amounts & Timing to Boost Survey Response

Recruiting people to participate in a survey is like hitting a baseball. Your goal is to connect, and execution is difficult. Just as batters face new challenges—harder-throwing pitchers and pitcher specialization—researchers face declining survey response rates, increasing costs, and competition for distracted subjects’ attention. For respondents, participating in surveys requires time and effort. Long surveys and those covering sensitive topics present greater burden. Prepaid incentives are one of the most reliable tools researchers can use to increase survey participation. This NORC team sought to determine the optimal incentive amount and whether splitting incentives across multiple mailings improves engagement or just adds cost without benefit.

4. Data Steward Competencies: Findings from a European Job Description Study

Sonraí is pleased to share the outcomes of a recent UCD MLIS Service Learning internship hosted in partnership with Asiera. Over four weeks, Ruoxin Liu, collected and analysed 43 data steward job descriptions from 13 European countries, producing both a structured dataset and a gap analysis report. The data steward profession is still developing across Europe. This study compared two established European competency frameworks, Skills4EOSC and RDNL, against the skills that European employers actually ask for in job postings. The aim was to identify two kinds of gap: skills the frameworks cover but employers rarely request, and skills employers expect but the frameworks do not address.

5. NIH Announces Winners of S-index Challenge to Advance Data Sharing Across Biomedical Research

The NIH Data Sharing Index, or S-index, is envisioned as a new metric that measures how effectively researchers make scientific data available for others to find, access, reuse, and build upon. The NIH S-index Challenge, a $1million prize competition, invited innovators to develop and validate approaches for measuring the scientific impact of data sharing while also emphasizing FAIR (Findable, Accessible, Interoperable, and Reusable) data practices and real-world research impact. This article announces the winners of the competition. The finalist teams developed innovative and complimentary approaches to measuring and rewarding high-quality data sharing, providing a foundation for the continued evolution of the S-index and future collaboration across the research community.

6. SQLite Query Explainer

Run SQL against a SQLite database and see what the query planner is really doing: the tool executes your query, then runs EXPLAIN QUERY PLAN and EXPLAIN against it and annotates every line of their output with a plain-English description. Powered by Python's sqlite3 module running in your browser via Pyodide — nothing you load or type leaves your machine.

7. A Holistic Approach to Professional Technical Digital Research is Possible

What does research look like in a world where data, software, and AI are an integral part of the research process? This post explores why closer collaboration between research software engineers, data stewards, and other digital Research Technical Professionals is essential for enabling robust, reproducible, and future-ready research.

Oldies but Goodies

Older resources that are still helpful

1. The Economic Impact of Open Science: A Scoping Review

This article summarizes a comprehensive scoping review of the economic impact of open science (OS), examining empirical evidence from 2000 to 2023. It focuses on open access (OA), open/FAIR data (OFD), open source software (OSS) and open methods, assessing their contributions to efficiency gains in research production, innovation enhancement and economic growth. Evidence, although limited, indicates that OS accelerates research processes, reduces the related costs, fosters innovation by improving access to data and resources and this ultimately generates economic growth. Specific sectors, such as life sciences, are researched more and the literature exhibits substantial gains, mainly thanks to OFD and OA. OSS supports productivity, while the very limited studies on open methods indicate benefits in terms of productivity gains and innovation enhancement. However, gaps persist in the literature, particularly in fields like citizen science and open evaluation, for which no empirical findings on economic impact could be detected. Despite limitations, empirical evidence on specific cases highlight economic benefits. This review underscores the need for further metrics and studies across diverse sectors and regions to fully capture OS's economic potential.

2. Open Science Resources

This list of resources, put together by Ruby Krasnow, organizes resources by topic including open science, fair data and data management, data management plans, computational reproducibility, open access publishing, and more. She also links to statistics and coding related links in another resource.

3. Keyboard Shortcuts in the RStudio IDE

This page provides a variety of helpful shortcuts when working in the RStudio IDE, including shortcuts for editing, completion, viewing, getting help, building, debugging, plotting, and more.

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.

• Texas Higher Education Coordinating Board - Research Operations & Systems Specialist (Contract - Remote)

• University of Bristol - UKRN Technical Systems Support Officer

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.

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.

Buy Me A Coffee

Welcome to Issue 54 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. A Milestone for Open Science: 500 Packages in the World Bank’s Reproducible Research Repository

The Reproducible Research Repository (RRR) is the public-facing platform of the World Bank’s Reproducible Research Initiative, which builds on the World Bank’s broader commitment to open data and open knowledge. This month, the World Bank’s RRR published its 500th reproducibility package—a milestone for open science and research transparency. Until a few years ago, finding the data and code behind a World Bank publication could be difficult: data citations were scarce, and code files, if published at all, were scattered across journal repositories, GitHub, and personal websites. RRR was created to change that by providing a single home for reproducibility packages associated with World Bank Group publications. As they mark this milestone, this article reflects on what the first 500 packages have taught us about making research more transparent, accessible, and reproducible in practice. The article also links to several public facing resources including a checklist for their reproducibility packages and a README generator.

2. From DAS to Deposit: Navigating Publisher Data Policies

If you have submitted a paper in the last few years, you have probably been asked for a Data Availability Statement (DAS). What that request means, however, varies widely. Some journals treat open data as a condition of publication. Others mainly want a statement, while leaving deposit optional or journal-dependent. A smaller group still has little more than encouragement, or no dedicated research-data policy at all. To make that landscape usable, the authors started from the CHORUS Publisher Data Availability Policies Index. CHORUS maintains a regularly updated list of publisher and journal data-availability policy pages, which is practical and broad enough to cover major commercial publishers, academic societies, and open-research platforms in one place. They coded every unique entry on that index into three groups by what authors must actually do: hard open-data mandates, mainstream DAS/tiers, and optional or no policy. Learn more about the groups in this resource.

3. filetree R package

filetree is an R package for declarative filetrees with filename validation and parsing. The package lets you define rules for how a project's folders and files should be named and organized, then automatically checks real directories against those rules. It’s built for people who have many files, that should be nested within a consistent folder structure. You describe each level of the folder hierarchy (for instance subject → date → data file) with simple templates, and the package flags any files that don't match your prescribed conventions.

4. Beyond Familiarity and Tradition: Software Choice in Psychology Statistics Courses Has Long-Term Implications for Research Skills and Open Science Practices

Software choice in psychology statistics courses remains debated. The long-term impact of software choice on researchers’ own use is unknown. This article sought to identify which statistical software researchers commonly used and the reasons for its use, with a particular focus on the extent to which use was related to what participants were formally taught. Psychology researchers from Canada and the United States (N=311) filled out an online survey asking about their statistical software use, preferences, and reasons for use. We conducted a content analysis to identify themes related to the use of specific software programs. The study finds that reasons for using statistical software differ based on the program, with a strong contrast between familiarity-based use (SPSS) and capacity-driven use (R).

5. Women as the First Open Scientists: Five Stories of the Neglected Contributions of Women in (Social) Science Reform History

Recent years have seen growing emphasis on “Open Science,” a movement to enhance research transparency, robustness, and accountability. Prompted by concerns over fraud, questionable research practices, and low reproducibility in psychological research that emerged in the 2010s, Open Science has been framed by some dominant advocates as a new approach to research in psychology and across the social sciences. However, women in social science have long practiced and promoted principles now hailed as innovative, although their contributions are often left out of mainstream narratives. This erasure is troubling, especially as feminist scholars have critiqued the increasingly exclusionary culture of mainstream science reform. This article traces the overlooked contributions of five pioneering women: Mary Whiton Calkins and her replication efforts in the 1890s; Helen Bradford Thompson Woolley, who advocated for analytical precision in the 1910s; Alice Bryan’s 1940s adversarial collaboration; Carolyn Wood Sherif’s metascientific work in the 1960s; and Leonore Tiefer’s promotion of conflict-of-interest declarations of sex research in the 2000s. The author documents how their contributions laid the intellectual foundations of what is now contemporary Open Science in the social sciences.

6. How to Share Qualitative Data - Seminar

Join The University of Sheffield Library on July 22nd, 1-2 PM BST via Google Meet where Dr Jenni Adams (University of Sheffield / MORPHSS project) will share three key strategies on how: Granular consent forms support different levels of openness and identifiability; Integrating processual consent strategies accommodate project dynamics; and Sharing transcripts with participants help build consensus on the content and presentation of the data. They will also explore situations in the context of debates around informed consent in the literature around qualitative data sharing.

7. Accessibility for Data and Data Repositories: Understanding and Applying the 2024 ADA Title II Rule

This report aims to support research data professionals and their institutions with the task of working to make repositories and the data in them digitally accessible. The report will help them understand the requirements from the rules, their implications, and the work and changes they may require. The report emphasizes that repository and data accessibility should not be an afterthought, but a fundamental part of the research lifecycle. The report prepared by the Research Data Access & Preservation (RDAP) Association's Digitally Accessible Data and Data Repositories Working Group.

Oldies but Goodies

Older resources that are still helpful

1. Are Commitments to Open Data Policies Worth the Paper They are Written On?

In this 2024 blog post Dorothy Bishop argues that open-data policies often prove hollow when tested, citing a Nature Communications superconductivity paper where physicist J. E. Hirsch spent over a year being stonewalled after requesting data behind an inconsistency he'd spotted, even though Nature's policy requires sharing, and Max Planck (whose researchers co-authored the paper) has its own open-data commitments. She argues vague "available on reasonable request" statements really mean "not available" and that refusing a simple request just makes things look more suspicious. Her conclusion: if institutions won't enforce their policies, they should stop claiming to have them.

2. Implementing Findable, Accessible, Interoperable, Reusable (FAIR) Principles in Child and Adolescent Mental Health Research: Mixed Methods Approach

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are a guideline to improve the reusability of data. However, properly implementing these principles is challenging due to a wide range of barriers. To further the field of FAIR data, this study aimed to systematically identify barriers regarding implementing the FAIR principles in the area of child and adolescent mental health research, define the most challenging barriers, and provide recommendations for these barriers.

3. How to Name Files Like a Normie

In this slide deck from Jenny Bryan, she provides guidance on naming files in a way that allows them to be both machine and human readable. Ultimately her core message is to just pick a consistent convention and stick to it, since any sensible convention beats none.

4. How to Export Analysis-Ready Survey Data

This blog post makes the case for designing data collection tools with the end result in mind, rather than fixing messy data after the fact. Using Qualtrics as an example, it walks through how to let your data dictionary guide the build (from variable naming to validation rules), so the data that comes out matches what you actually planned to collect. The upfront investment in planning pays for itself many times over in cleaning time saved.

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.

• The Jed Foundation - Senior Data Management Specialist (Remote)

• Indiana University - Research Data Librarian

• Appalachian State University - Research Data Librarian and Institutional Repository Manager

• Texas 2036 - Data and Systems Analyst

• GESIS - Leibniz Institute for the Social Sciences - Research Infrastructure Specialist (Postdoc) for Trusted Research Environments (DSS-26)

• Alfred Wegener Institute - PANGAEA Data Curator in Data Resilience Project

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.

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.

Buy Me A Coffee

Welcome to Issue 53 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. Data Analysis Journeys

This open access book contains data analysis journeys to practice your wrangling, visualisation, and analysis skills (using R) in a less structured format than the main PsyTeachR books. The authors have designed these tasks as a bridge between the structured learning in the core course book chapters and your assessments. The authors present you with a new data set, show you what the end product should look like, and see if you can apply your data wrangling, visualisation, and/or analysis skills to get there. Data analysis is all about seeing the data you have available to you and identifying what the end product needs to be to apply your visualisation and analysis techniques. You can then mentally (or physically) create a checklist of tasks to work backwards to get there. There might be a lot of trial and error as you try one thing, it does not quite work, so you go back and try something else. If you get stuck though, the book provides a range of hints and task lists that you can unhide, then the solution to check your attempts against.

2. Where’s the Evidence that Respondents Understand Your Survey Questions?

Survey researchers try to align respondent question interpretations with their own by applying theoretical rules, such as writing questions simply, concretely, and without biasing words or double-barreled inquiries, or via post hoc plausibility checks. Few, however, conduct ex ante empirical evaluations of their application of these rules. The widely recognized best empirical approach is “cognitive debriefing,” that follows a survey question with a detailed conversation with the respondent. Unfortunately, this time-consuming procedure is rarely used, especially for novel surveys on online platforms. This paradoxically leaves the veracity of the discipline’s most used empirical methodology depending largely on theoretical assumptions. For cognitive debriefing to live up to its intended potential, the authors of this study first formalize the essential interpretation-related assumptions underlying all survey questions. They then automate cognitive debriefing to satisfy these assumptions using a specially-tuned chatbot, apply it to larger numbers than heretofore possible, analyze the collection of transcripts, and summarize the interpretations it reveals. By iteratively rewriting questions and rerunning this procedure, researchers can help ensure respondents understand questions as they do. The authors also apply their procedure to some of the most common survey questions and demonstrate large disconnects between researcher intentions and respondent understandings. The authors make available easy-to-use open source software that implements their suggestions.

3. data-dict.yaml

data-dict.yaml is a data dictionary specification that describes a collection of related tables: their contents, constraints, connections, and the specialised vocabulary you need to understand them. It is designed to be a living document, co-written by humans and agents, that tracks your understanding of a dataset as it evolves. data-dict.yaml is designed to be lightweight. It doesn’t attempt to precisely describe every possible type of metadata in a machine-readable way. Instead it focuses on precisely recording the most important components, leaving the remainder to plain text fields that require a human or agent to interpret. This means that data-dict.yaml doesn’t itself do data cleaning, but it is a useful complement to tools that do.

4. DataCite DOI Re-Curation Watch

DataCite DOI metadata should evolve over time as new related identifiers and resource types emerge, and the authors point out that a robust versioning/provenance system for tracking these changes already exists within DataCite's activities API — repositories don't need to build one themselves. Because that raw API output is hard to browse, Metadata Game Changers built a "Re-Curation Watch" tool that lets users pull recently updated records from a repository and see a simple history of what changed and when. The tool is now open for public testing, with the authors inviting feedback and sharing examples of repositories already using metadata updates as a best practice.

5. Eleven Strategies for Making Reproducible Research and Open Science Training the Norm at Research Institutions

Reproducible research and open science practices have the potential to accelerate scientific progress by allowing others to reuse research outputs, and by promoting rigorous research that is more likely to yield trustworthy results. However, these practices are uncommon in many fields, so there is a clear need for training that helps and encourages researchers to integrate reproducible research and open science practices into their daily work. Here, the authors outline eleven strategies for making training in these practices the norm at research institutions. The strategies, which emerged from a virtual brainstorming event organized in collaboration with the German Reproducibility Network, are concentrated in three areas: (i) adapting research assessment criteria and program requirements; (ii) training; (iii) building communities. The authors provide a brief overview of each strategy, offer tips for implementation, and provide links to resources. They also highlight the importance of allocating resources and monitoring impact. Their goal is to encourage researchers – in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees – to think creatively about the many ways they can promote reproducible research and open science practices in their institutions.

6. Modern Python Project Management for Researchers with uv

The Python programming language is enjoying great popularity in Scientific Computing due to its ease of use and its vast ecosystem of available software packages. However, due to the explosive growth of the number of available Python packages, complex dependency trees are becoming an increasing concern for the maintainability and reproducibility of Python software. This workshop provides a practical guide to uv, a powerful tool designed to facilitate the whole experience of working with Python code and dependencies. The 90 minute online workshop, happening this Thursday July 16th, will introduce the core functionalities of uv, from the efficient execution of standalone scripts and the management of project environments using lockfiles to structuring, building, and shipping your own project for public distribution. By the end of the course, researchers will have a streamlined toolkit to ensure their computational work is both sustainable and easily shareable with the scientific community.

7. ECR Asks: Dr. Ed Ivimey-Cook

‘ECR Asks’ is a series of Q&A sessions where Oakleigh Wilson speaks with experienced SORTEE members to explore their journey in, and perspectives on, open science and transparent research. With the goal of supporting early career researchers, this series aims to answer big questions and share practical insights on navigating the ever evolving landscape of open science and academia. In this conversation, Ed shares his time, experience, and tips. This conversation was an insightful glimpse into the challenges and future of code review with valuable advice for open science focused ECRs.

Oldies but Goodies

Older resources that are still helpful

1. National Library of Medicine Data Glossary

This resource connects and defines concepts, services, and tools relevant to librarians working in data-driven discovery. A definition, relevant literature, and web resources accompany each term along with links to related terms. Search by term or keyword or browse the terms listed. The Data Glossary is created and maintained by the NCDS.

2. GREI Collaborative Webinar Series on Data Sharing in Generalist Repositories

On this page you can access resources from a series of four presentations and panel discussions by generalist repositories to learn about available repository resources and best practices for sharing NIH-funded research. Presented by the members of the NIH Generalist Repository Ecosystem Initiative (GREI): Dryad, Dataverse, Figshare, Mendeley Data, Open Science Framework, and Vivli.

3. Open Science + Data Management Policies

This resource contains slides from a training webinar on Open Science practices in LD research, put together by the Florida Learning Disabilities Research Center. Topics covered include effective management of research data, federal data policies, and data documentation.

4. Cultural Obstacles to Research Data Management and Sharing at TU Delft

Research data management (RDM) is increasingly important in scholarship. Many researchers are, however, unaware of the benefits of good RDM and unsure about the practical steps they can take to improve their RDM practices. Delft University of Technology (TU Delft) addresses this cultural barrier by appointing Data Stewards at every faculty. By providing expert advice and increasing awareness, the Data Stewardship project focuses on incremental improvements in current data and software management and sharing practices. This cultural change is accelerated by the Data Champions who share best practices in data management with their peers. The Data Stewards and Data Champions build a community that allows a discipline-specific approach to RDM. Nevertheless, cultural change also requires appropriate rewards and incentives. While local initiatives are important, and the authors discuss several examples in this paper, systemic changes to the academic rewards system are needed. This will require collaborative efforts of a broad coalition of stakeholders and the authors will mention several such initiatives. This article demonstrates that community building is essential in changing the code and data management culture at TU Delft.

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.

• University of Bath - Research Data Librarian

• RTI International - Senior Program Manager, Center for Data Interoperability

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.

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.

Buy Me A Coffee

Welcome to Issue 52 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. Introducing Open Data Format: A Platform-Independent, Non-Proprietary, Metadata-Enriched, Multilingual Data Format for Tabular Data

This paper introduces the Open Data Format (ODF), a new, non-proprietary, metadata-enriched, and zip-compressed data format for tabular data alongside statistical software packages. It embeds structured, multilingual metadata directly within the data file, using the well-established DDI-Codebook standard (DDI Alliance, 2014), and enables access to both data and documentation within common statistical environments. Dedicated software packages for R, Stata, and Python allow users to seamlessly import, export, and explore both data and metadata, moving documentation significantly closer to the user. The data format is specified as a CSV file for the data and an XML file containing the metadata, both compressed into a zip file with the '.odf.zip' extension. Due to space limitations in the journal, the authors also provide a more detailed working paper, and software and additional resources can be found here.

2. Education Data Atlas - A Guide to Federal Datasets, Grant Funding Formulas, and Policy Questions

Every year, billions of federal dollars flow to more than 13,000 school districts based on a complex mix of demographic and financial data. The Education Data Atlas is your guide to these essential datasets. Built for researchers, education leaders, and policy analysts, the Atlas maps out the federal K-12 dataset landscape, explains how they answer key policy questions, and shapes the resources available to school districts and students. The Education Data Atlas provides three distinct ways to navigate complex federal K-12 data depending on your goals. You can explore Individual Datasets, start with Policy Questions to find the exact data files that answer them, or trace how administrative data calculates major Federal Grants.

3. From Quest to Request: Chasing Data in the Wild

In the era of open science, large, high-quality open-access datasets play a pivotal role in facilitating interdisciplinary research, fostering data inclusivity, and enhancing transparency and engagement. This case study highlights challenges that can be faced when attempting secondary data analysis and provides practical recommendations for promoting more equitable access to datasets.

4. R-Course: R for Social Scientists

The course is designed for academics in different career stages (from PhD students to professors) and with different backgrounds. As long as you have had some experience working with dataframes then it does not matter if you are a qualitative researcher that has never used R, or a quantitative researcher that wants to learn new topics or move out of SPSS/STATA. You all are welcome!

5. Beyond Platforms and Protocols: Funding the Human Layer of Infrastructure

This article makes the case that scholarly infrastructure (like preprint servers and open-science tools) doesn't fail from bad technology — it fails because nobody funds the human work of advocacy, education, and community-building needed to get researchers to actually adopt and trust it. The author argues funders should create dedicated grants for that "human layer" of work, rather than treating it as an afterthought to building the platforms themselves.

6. A Surfeit of Sources: An Annotated Bibliography of Data Management Books for Librarians

Over the past decade, more than thirty books have been published which seek to provide data management foundational and continuing education materials for an academic librarian audience. This annotated bibliography centrally collects and reviews these titles, providing not only a quantitative overview of publisher, price, and availability but also a topical overview for each book and a brief critical summary. Concluding with recommendations for the most useful titles for targeted audiences, the authors address trends, gaps, and limitations of the present corpus.

7. AI Didn’t Change the Purpose of Metadata

Metadata is getting all the spotlight nowdays, thanks to AI craze. For decades, metadata has served four primary purposes: Classify, Describe, Guide, Control. These four purposes are still as relevant today as they were when organizations were managing paper files, libraries, and the first relational databases. In this article, Olga Maydanchik describes the four primary purposes and suggests that what has changed is not the purpose of metadata, but the number of consumers. Metadata is no longer created only for people. Increasingly, it is consumed by machines.

Oldies but Goodies

Older resources that are still helpful

1. SIPS Products

This site provides a list of projects that started or developed at SIPS (Society for the Improvement of Psychological Sciences). Projects include resources, datasets, training materials, communities, videos, templates, tools, papers, and more.

2. Teaching Research Data Management for Students

Sound skills in managing research data are a fundamental requirement in any discipline of research. Therefore, research data management should be included in academic education of students as early as possible. The authors have been teaching an interdisciplinary full semester’s course on research data management for six years. They report how they established the course. They describe their competency-based approach to teaching research data management and the curriculum of topics that they consider essential. They evaluate their approach by a survey done among the participants of the course and summarize the lessons they learned in teaching the course.

3. Template for Data Cleaning Guidance and SOP

This template from Murdoch Children’s Research Institute is to be used to create a study specific guideline or Standard Operating Procedure (SOP) for data cleaning. It can apply to the entire study/trial or to one analysis/database lock in a larger study/trial. This document provides an overview of the main things to consider when writing such a guideline and/or when planning your data cleaning. This document provides an outline and description of required sections, but should be modified based on the specific section. Sections that are not applicable can be deleted. Note that this list is not comprehensive as each study/trial will have its own study-specific needs regarding key data and essential data checks to be performed.

4. Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, the authors analysed 39 use cases and current practices of data versioning across 33 organisations. The authors noticed that the term ‘version’ was used in a very general sense, extending beyond the more common understanding of ‘version’ to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, the authors developed six foundational principles for versioning of datasets that can guide the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.

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.

• Radboud University Medical Center - FAIR Data Steward

• University of South Hampton - Knowledge Exchange and Enterprise Fellow PSDI Research Data Specialist

• European Open Science Cloud Czech Republic - Data Steward

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.

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.

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Welcome to Issue 51 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. Using Routinely Collected Data for Research Purposes: Challenges and Mitigation Strategies

Increasing availability of large routinely collected datasets presents many possibilities to answer more questions about health and disease, and at a faster pace. These opportunities are exciting, but, without the necessary expertise, well-intentioned researchers can unwittingly fall into traps that make their work indistinguishable from that of less well meaning researchers. This article describes important challenges that arise in the analysis of routinely collected data and offers mitigation strategies to improve the trustworthiness of experimental and observational studies with the aim of explanation or prediction using classical statistical methods or AI algorithms. It also provides a roadmap to support researchers in conducting analyses on routinely collected data that produce more reliable estimates.

2. Research Culture: The Missing Conversation

The article argues that while research institutions invest heavily in policies, procedures, and compliance systems, these alone cannot create genuine integrity. Culture is what shapes behavior when rules are silent or ambiguous. Poor research culture can manifest in authorship disputes, exclusion of contributors, and power imbalances, none of which may violate formal policies but all of which erode trust and collaboration. The author calls on institutions and leaders to go beyond asking "Do we have the right policies?" and start asking whether researchers feel safe, valued, and empowered to do the right thing even without explicit rules.

3. Creative Uses in REDCap

This is a recording from a Lunch, Learn & Collaborate session from FSU Research Computing Center. The topics covered in this webinar included piping, action tags, smart variables, and more. You can find more REDCap Lunch and Learn recordings on their YouTube page.

4. Trust, FAIRness and Impact: Where Electronic Research Notebooks Fit In

This Open Research Perspective, contributed by Dr. James Bird, Technical Specialist in Research IT, explores the relationship between research trustworthiness, evaluability and impact, using data accessibility as one practical lens. It argues that while Open Research has improved the reach of research outputs, greater attention is still needed to ensure those outputs are genuinely assessable, reusable and trusted. Against that backdrop, James considers how Electronic Research Notebooks (ERNs) could help strengthen research workflows and support more FAIR, trustworthy research.

5. ORWG Guide: Reusing Open Datasets

This is one of a series of open research guides aimed at researchers at London School of Economics and Political Science (LSE) (although much of it is applicable to external researchers as well) created under the auspices of LSE's Open Research Working Group (ORWG). With the growth in researchers making their data openly accessible, there is more data than ever available for re-analysis, re-use, and replication studies. This short, one-page guide/checklist gives a short, step-by-step overview of how to go about reusing an existing open access dataset to conduct new research.

6. Preliminary Evidence Linking Open Science to Research Integrity

In this post, the authors ask the question, are open science behaviors really less common in articles that are associated with papermills? To capture open science behaviors, the authors draw on the PLOS Open Science Indicators (OSI) v10 dataset, which includes measures of code sharing, citation of author-generated protocols, preregistration, and preprinting. The dataset covers approximately 139,000 PLOS research articles published since 2018, along with a comparator set of roughly 38,000 articles drawn from PubMed Central.

7. Data Management for Beginners - Recording

This is a recording from the Reveal Research workshop that happened June 12th, 2026. The theme focused on preventing data suffering by thinking about how you will manage your data in advance.

Oldies but Goodies

Older resources that are still helpful

1. EOSC Open Science Observatory

The EOSC (European Open Science Cloud) Open Science Observatory is a platform that tracks how Open Science is progressing across Europe. It provides a clear, data-based overview of policies, practices, and trends in Open Science. Designed for policymakers, research institutions, and other stakeholders, the EOSC Open Science Observatory offers easy-to-understand visualizations and key insights. It supports better decision-making, helps identify progress and gaps, and contributes to a more open and transparent European Research Area.

2. The Open Science Utopia: How Transparency Can Improve Science

What if one of the biggest obstacles to scientific progress isn't a lack of ideas, but the way research itself is organized? In this thought-provoking talk from November 2025, Heidi Seibold shares her journey from researcher to an Open Science advocate. After experiencing firsthand the pressures of academia's "publish or perish" culture, she began questioning a system that rewards publication quantity over research quality. Through personal stories and real-world examples, Heidi reveals how greater transparency, reproducibility, and collaboration can transform research and accelerate solutions to humanity's biggest challenges. This talk explores a compelling vision for the future: an Open Science ecosystem where knowledge is shared freely, research is trustworthy, and scientists are empowered to build on each other's work for the benefit of society.

3. Reframing Repositories: Why Repositories Matter More Than Ever!

To highlight the critical importance of repositories, COAR (Confederation of Open Access Repositories) is publishing a “Narrative Series”. This series provides several compelling, evidence-based portrayals that, taken together, build a powerful case for repositories as essential infrastructure for scholarly communications and the global knowledge commons.

4. Fundamentals of Wrangling Healthcare Data with R

This course will review some of the tools of the trade, namely, R’s tidyverse - a collection of R packages designed with a common framework to aide in common data wrangling and data management tasks. Data Wrangling is one subset set of skills within the Data Science Process. This course will carefully investigate how decisions made while collecting and preparing the data have down-stream effects on model performance. The primary resource is an SQLite database made from downloading various files from the NHANES data source. The goal here is to try to give that experience of connecting to and working with a database with R. Collecting data from a potentially database, running statistical analyses, and making inferences as to which features would perform well when fitting predictive models.

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.

• Project Evident - Associate Director, Data Hub (part-time, remote)

• City University of New York - Institutional Research Specialist (Data Integration)

• Princeton University - Research Data Management Specialist

• Minnesota Population Center - Statistical Programmer

• American Heart Association - Project Manager, Research and Data Workflow (remote)

• University of South Hampton - Knowledge Exchange and Enterprise Fellow

  PSDI Research Data Specialist

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.

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.

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Welcome to Issue 50 of the RDM Weekly Newsletter (aka our 1 year anniversary)!

One year ago I had the idea to start a weekly newsletter as a way to more systematically publicly share all of the research data management resources I had collected over the years, as well as the steady stream of new ones I came across each week. At first I worried that keeping up with a weekly newsletter would be a struggle, and that I would run out of resources to share. Yet, a year and 50 issues later, here we are! I am grateful to everyone who has joined me on this ride, and for supporting this work by liking, sharing, and subscribing! Here’s to another great year of learning together! 🎉

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

✅ ICYMI Highlights from Past Issues

• For just this issue, rather than doing the usual “oldies but goodies” category, I am highlighting some of the most popular resources shared in the first 50 issues (just in case you missed these!)

✅ 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. Hello Data Science

This open access book is intended for anyone who wants to take on their first data science project to look for patterns, relationships, and meaning in data - without being intimidated while still being challenged. The book is currently a work in progress. The first 10 chapters that have been released focus on essential data wrangling skills including transformation, aggregation, joining, pivoting, exploratory data analysis, and more. As education with generative AI use evolves, the authors ideas will too. Their current approach is to start the old school way. The authors want readers to understand the basics before they rely on AI.

2. Framework for the Governance of Indigenous Data: HASS and Indigenous Research Data Commons

The Framework for the Governance of Indigenous Data, from the Australian Research Data Commons, is a guide for the ethical, inclusive, and effective management of Indigenous data within the Humanities, Arts, Social Sciences and Indigenous Research Data Commons (HASS and Indigenous RDC). It responds to long-standing calls from Aboriginal and Torres Strait Islander communities for greater control over data that affects their lives, lands, cultures, and futures. The Framework is the result of extensive consultation, co-design, and collaboration with Indigenous data custodians, researchers, institutions, and policymakers. It is intended to be adopted across all HASS and Indigenous RDC activities. The Governance Model identifies 5 foundational elements which underpin the Framework’s guidelines and practices, which provide actionable strategies for institutions and communities to embed Indigenous governance across the data lifecycle.

3. CREATE-EBP Researcher Technology Survey Results

Researchers increasingly use technology and digital tools in school-based research. Few studies have systematically examined how these tools are used in school-based settings. The CREATE-EBP study documents researchers’ use of participant-facing technology and digital tools, related challenges, lessons learned, and future possibilities. 69 school-based researchers participated in this exploratory study. Participants completed an anonymous online survey about technologies and digital tools used in school-based research. The survey included close-ended and open-ended questions to examine both patterns of use and researchers’ experiences. This summary document highlights preliminary findings from this study.

4. What Makes a Good Data Steward?: Beyond Data Management Skills

These are slides from a presentation at the DANS Open Day 2026. How do we train data professionals in the ‘transversal’ or ‘soft’ skills that they need to conduct their tasks and responsibilities effectively. Why? What? How? When? The presentation showcases the RDNL (Research Data Netherlands) Competency Framework for data professionals and explains how transversal skills fit within this. It briefly outlines some of the training initiatives that aim to address some of the already-identified training gaps that exist in relation to transversal skills.

5. FAIR Data Principles in Energy Research: An Empirical Analysis on Current Practices, Researcher Attitudes, Application Barriers and Practical Implications

For a successful energy transition, the FAIR data principles provide an essential framework for managing and reusing energy related data for research, yet their implementation remains limited. In this explorative study, the authors empirically examine how energy researchers engage with energy data in relation to FAIR data principles, research data platforms, and open science practices, while acknowledging the diversity of the energy research community. Fifteen semi-structured expert interviews and a workshop with eight researchers were conducted, complemented by an explorative (typifying) qualitative content analysis. The study’s indicative results reveal key characteristics of the energy research community as perceived by the interviewees, which is marked by a strong inter- and transdisciplinary orientation as well as a pronounced normative orientation towards fostering a just energy transition. Furthermore, the author’s identify five attitude types towards FAIR data principles (1) Enthusiasts, (2) Idealists, (3) Pragmatists, (4) Strategists, and (5) Resisters along with perceived barriers to their application on the micro (individual), meso (institutional), and macro (systemic) levels. The authors discuss specific measures to overcome these barriers for (i) publishers and funding agencies, (ii) research institutions, and (iii) platforms addressing different levels of the research landscape.

6. OSF Essentials: Monthly Deep Dive

These free webinars, occurring on the last Thursday of every month, are focused sessions on different OSF topics, built around the questions, workflows, and practical needs users most often want help with. The next monthly deep dive is “Licensing Basics on the OSF”, occurring Thursday June 25th at 10am central time.

7. What to Say When They Ask You About Research Culture

This article is aimed at helping researchers confidently answer questions about research culture, including questions that increasingly appear in job interviews, funding applications, REF submissions, and conference panels. It breaks down the concept by encouraging researchers to reflect on their own values and ambitions, then connect those to their institution's priorities and sector-wide frameworks, using the University of Glasgow's five research culture priorities (Research Recognition, Collegiality and Teams, Research Integrity and Ethics, Open Research, and Career Development) as a structural anchor. The piece emphasizes that articulating one's contribution to a positive and inclusive research culture is increasingly tied to funding success, and that every researcher has a genuine role to play in shaping the culture around them.

ICYMI Highlights From Past Issues

Some of the most popular resources shared in the past year of RDM Weekly

1. Checklist for Reusable Code (From Issue 48)

Reusable code is well-documented, human-readable, portable, organized, and version-controlled. Creating reusable code helps you, your collaborators, and the broader open science community by making your work easier to understand, adapt, reproduce, and cite. Use this quick checklist from Cornell Data Services for a fast self-check when sharing code for your project. For more guidance and recommended practices for each item, the site also includes an expanded checklist.

2. Bare Necessities of Data Management (from Issue 20)

There are so many data management practices that can help you better organize your project, yet a team’s ability to “do it all” is really limited by factors such as funding, timing, team size, and expertise. Therefore, it is important for teams to consider what practices are feasible as well as which ones will give them the largest return on investment. This blog post reviews a list of core practices that many teams can implement early on in a project that will lead to better data outcomes.

3. Ten Simple Rules for Effective Research Data Management (from Issue 37)

Advances in information technology, digitalization, database volume, the internet, high-throughput measurement technology, and artificial intelligence (AI) have profoundly transformed research. In the 20th century, it was common for a study or an experiment to yield one single file (e.g., a table). Today, many research projects yield many files, often created by multiple collaborators and are often valuable for secondary use. Furthermore, scientific knowledge is currently generated not only through hypothesis-driven statistical inference but also using (un)supervised data mining and AI techniques applied to existing resources. These developments require effective research data management (RDM) at both project and institutional level. Several publications of the “Ten Simple Rules” series offer guidance on RDM subdomains. However, they focus on project-related RDM topics. Thus, this Ten Simple Rules for Effective Research Data Management provides a condensed reference of significant RDM topics applicable at higher organizational levels, arranged in a logical sequence corresponding to the research data life cycle. The rules are derived from the authors’ diverse expertise in statistics, genomics, bioinformatics, public health, epidemiology, and research data management consulting. They may serve as a reference for institutions, researchers or professionals regardless of field or career level.

4. How to Make a Data Dictionary (from Issue 29)

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.

5. READMEBuilder (From Issue 44)

REAMEBuilder is a Shiny application that walks researchers through building a high-quality README file for research datasets and code. It automatically describes tabular data columns (ranges, levels, NAs), detects R package dependencies from scripts, supports script run-order documentation, and exports a formatted README.md.

6. Reproducible Code Guide (from Issue 28)

This guide, from the British Ecological Society, covers the basic tools and information you need to start making your code more reproducible. Most examples are in R and Python, with a few in Julia, but the tips should apply to any programming language. It covers aspects that fall within the reproducibility ecosystem — Documentation, Automation, and Structure.The guide encourages you to start by implementing one or more aspects of this guide and then continue building on your practices over time to improve the reproducibility of your code and research.

7. Readable, Reliable, Reusable: A Guide to Clean R Code (from Issue 16)

Clean code is code that can be easily understood—by everyone on the team, and by future you. It’s readable, maintainable, easier to debug, and scalable. But writing clean code isn’t automatic—it’s a skill that improves with practice, reflection, and shared standards. In this blog post, Jacci Ziebert shares practical tips and examples for writing clean R code. Many of these ideas are inspired by Robert C. Martin—aka “Uncle Bob” in his book Clean Code and adapted to fit the patterns and challenges of R programming. The blog focuses on three key areas: naming practices, function practices, and commenting practices. These aren’t just stylistic preferences—they’re tools for building code that lasts.

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.

• Uncommon Schools - Temporary Data Request Analyst

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.

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.

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Welcome to Issue 49 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. Data Reuse is the Sincerest Form of Flattery

This article argues that existing post-publication assessment methods like commentary articles and article to article citations capture "excitingness" rather than the robustness of methods or validity of results. It proposes that "article to dataset to article" citations are a far more "trust dense" signal, since reusing someone's data requires deeply validating their work and staking your own reputation on it. Finally, the article notes that while detecting data reuse has historically been nearly impossible, new reasoning LLMs can now reconstruct these data citations, making this a viable quality metric at scale.

2. Sonraí, DANS & CaSDaR Masterclass - Advocacy Skills for Data Stewards

Join Sonraí, DANS, and CasDaR on Monday June 22nd for a unique masterclass focused on effectively influencing stakeholders and advocating for data stewardship. This practical 2-hour webinar will introduce participants to key techniques for communicating ideas more persuasively with senior stakeholders and non-expert audiences. Participants will learn how to structure a clear business case, use plain English to improve understanding, and adapt their communication style to build stakeholder understanding and buy in. The session will provide practical tips that can be applied immediately when presenting recommendations, proposals and initiatives. Participants will explore these techniques in the context of communicating specialist or technical information to wider stakeholder groups.

3. Improving Acknowledgments Sections to Better Credit Research Contributors

Formal recognition of research contributions is critical for career advancement and the allocation of research funding. However, some contributions are mentioned only in the acknowledgments section, which are not indexed by scholarly databases, resulting in little recognition for those involved. The authors contextualize this shortfall in terms of contributorship, the movement to recognize specific research contributions rather than rely solely on authorship. Broadening the range of recognized individuals is currently advanced largely through reducing authorship restrictions and unbundling manuscripts into smaller elements, such as datasets and protocols, that receive their own attributions. Here they focus on a complementary path, enhancing the contents and metadata of acknowledgments sections. Capitalizing on existing infrastructure and standards, the authors propose: 1) when acknowledging individuals, authors include ORCIDs (subject to the acknowledgees’ approval) and provide CRediT information where applicable; 2) publishers solicit identities of acknowledgees in a similar way to how they do so for authors in their submission portals; and 3) publishers include metadata of acknowledgees in JATS-XML files. Implementing these steps should encourage scholarly databases to index non-author contributors. The ensuing increase in visibility for research contributors, such as technicians and library professionals, should result in greater recognition of non-author roles.

4. Hugging Face Dataset Lineage Explorer

As Daniel van Strien discusses in his LinkedIn post, datasets on Hugging Face (HF) are often cleaned versions, translations, filtered subsets, or format conversions of something that already existed. You wouldn't see this from most of the metadata. The HF Hub has a source_datasets field in the dataset card YAML which almost nobody fills it in. To improve data lineage, Daniel built an explorer that infers the missing lineage by comparing datasets and allows you to browse the inferred lineage for any dataset.

5. R for the Rest of Us Podcast Episode 30: Sara Altman and Simon Couch

Most coding assistants treat data quality as a speed bump. You hand them a messy CSV, they squint at it, and a moment later they've shipped some plausible-looking analysis built on a quietly broken foundation. Posit's new AI agent takes a different stance: it pauses, asks you the awkward questions, and treats the messy bits as the point of the work, not an obstacle to it. In this recording, David Keyes sits down with Sara Altman and Simon Couch to discuss Posit Assistant.

6. Research Security and Open Science: The Odd Couple

The institutional and political push towards both open science and research security appears like an unhappy marriage, reliving the essential tension between secrecy and openness in scientific research. Yet, the authors of this article hold that it is possible – and much needed! – to navigate this fraught territory in order to integrate research security within open science. By enriching our understanding of openness, the authors hope to ease the tension between research security and open science and eventually push forward the values that originally guided the open science movement.

7. Better Code, Better Science

Making science more reproducible and transparent is key to improving public trust in science. Because science is increasingly a computational enterprise, improving the quality of scientific research code is essential to making science more reproducible. Increasingly, this code is being written with the help of AI assistants, which can increase productivity but introduces the potential for errors. This open access book will provide a thorough guide to using AI-assisted coding techniques to generate scientific code that is readable and robust and that can provide reproducible answers to scientific questions.

Oldies but Goodies

Older resources that are still helpful

1. Understanding Metadata: What is Metadata, and What is it For?: A Primer

Metadata, the information we create, store, and share to describe things, allows us to interact with these things to obtain the knowledge we need. This primer by Jenn Riley of McGill University Library offers a comprehensive overview of metadata, covering topics such as metadata types, standardization, and use in the cultural heritage sector and in the broader world. The Primer is accompanied by plentiful examples of metadata at work.

2. The Principles of Open Science Monitoring

To fully take advantage of the adoption of the 2021 UNESCO Recommendation on Open Science, transparent and representative monitoring must be put in place to drive and support the intended change. It is also vital to identify effective actions and priority gaps. To compensate for the lack of global guidelines on open science monitoring, the French Ministry of Higher Education and Research initially brought together a group of French experts (Université de Lorraine, Inria) to work on a proposal for common monitoring principles. This text served as a basis for a conference which gathered international experts at the Paris UNESCO headquarters in December 2023, leading to the Open Science Monitoring Initiative (OSMI). OSMI and UNESCO then conducted an international consultation to gather opinions from around the world to ensure that the Principles meet a variety of needs, approaches and contexts worldwide. These Principles focus on three key pillars: (1) relevance and significance, (2) transparency and reproducibility, and (3) self-assessment and responsible use.

3. Are Leadership and Management Essential for Good Research? An Interview Study of Genetic Researchers

Principal investigators are responsible for a myriad of leadership and management activities in their work. The practices they employ to navigate these responsibilities ultimately influence the quality and integrity of research. However, leadership and management roles in research have received scant empirical examination. Semi-structured interviews with 32 National Institutes of Health (NIH)-funded genetic researchers revealed that they considered leadership and management essential for effective research, but their scientific training inadequately prepared them. The authors also report management practices that the researchers described employing in their labs, as well as their perceptions of a proposed intervention to enhance laboratory leadership. These findings suggest best practices for the research community, future directions for scientific training, and implications for research on leadership and management in science.

4. SQL Style Guide

These guidelines are designed to be compatible with Joe Celko’s SQL Programming Style book to make adoption for teams who have already read that book easier. This guide is a little more opinionated in some areas and in others a little more relaxed. It is certainly more succinct where Celko’s book contains anecdotes and reasoning behind each rule as thoughtful prose.

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.

• University of Illinois Chicago - Institutional Research Analyst

• Wageningen University & Research - Research Engineer Data Management

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.

🎉 Next week is the 1 year anniversary of this newsletter and I would love to reach 1,000 subscribers to celebrate this milestone! If you enjoy this newsletter, please share it with colleagues, friends, and anyone else who you think may benefit from reading it!

And as always, you can also support this work through Buy Me A Coffee.

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Welcome to Issue 48 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. Checklist for Reusable Code

Reusable code is well-documented, human-readable, portable, organized, and version-controlled. Creating reusable code helps you, your collaborators, and the broader open science community by making your work easier to understand, adapt, reproduce, and cite. Use this quick checklist from Cornell Data Services for a fast self-check when sharing code for your project. For more guidance and recommended practices for each item, the site also includes an expanded checklist.

2. A Decade of Open Data: Progress, Challenges, and How Institutions Can Support

For research organisations working to strengthen open science practices, the tenth anniversary of the FAIR principles is a timely milestone to take stock of what has changed and where more efforts are needed. As open data policies evolve, AI adoption accelerates, and expectations for data quality intensify, understanding researcher attitudes has never been more important. This Springer Nature blog post reviews a decade of progress on open data in academic research, covering what's improved, what obstacles remain, and how institutions can better support researchers in sharing their data effectively.

3. Today I Learned in R: RStudio Snippets with Jadey Ryan

In this video from Equitable Equations, the hosts are joined by data scientist Jadey Ryan to learn about a hidden gem of an RStudio feature: code snippets. Snippets allow you to automate the insertion of commonly used code. Find the gist that Jadey references here.

4. Finding a Way Forward: How to Create a Strong Legal Framework for Data Integration

This resource was created by Actionable Intelligence for Social Policy (AISP) to support the essential and challenging work of exchanging, linking, and using data across government agencies. It is based on the experience of practitioners who, collectively, have decades of experience developing strong data governance and legal frameworks to support cross-sector data integration. Each section frames out key concepts and then provides prompts for discussion to move toward action.

5. With Qualitative Research, the Risks of Data Sharing Can Outweigh the Rewards

PNAS is among the many journals that have adopted data sharing policies, with the goal of “promoting transparency, reproducibility, and accountability in research.” However, this editorial discusses how PNAS editors recently made an exception to journal guidelines—setting a precedent with important implications for the viability of qualitative research, particularly in the context of social media, surveillance culture, and artificial intelligence (AI).

6. Demystifying Data Journals: Experiences of Data Journal Editors - Recording

This webinar, delivered by the N8 Centre of Excellence in Computationally Intensive Research (N8 CIR), explored publishing in Data Journals, featuring the perspectives of four data journal editors. Data journals are a format of publication that publish datasets or ‘data papers’ rather than conventional research articles. They offer a route to increase the visibility of research data outputs whilst also rewarding creators with a peer-reviewed publication, credit, and increased opportunity for citation (Walters, 2020). Links to slides for all four presentations are included.

7. Code Sharing and Reproducibility in Survey-Based Social Research: Evidence from a Large-Scale Audit

In this study, the authors assess the reproducibility of articles using the European Social Survey (ESS), a large-scale repeated cross-sectional dataset widely used across the social sciences. Drawing on more than 1000 ESS-based articles published between 2015 and 2020, this study investigates whether authors share their code for reproduction purposes and whether published results are reproducible. The study finds that only about one in three authors (35%) share code. From the articles with code, the study randomly selected 100 which reported 699 results. Of these 699 results, about half (51%) are numerically reproducible, while the others either fail (23%) or are different (26%). For those that are different, numerical deviations are usually minor and do not indicate systematic bias. Overall, about one in six published results (18%) is exactly reproducible. Reproducibility failure mostly stems from unavailable, poorly documented, or incomplete code. The authors propose low-cost measures for authors, editors, journals and data providers to improve code availability and reproducibility in large-N observational social research.

Oldies but Goodies

Older resources that are still helpful

1. GREI Metadata Recommendations from DataCite Schema Version 4.6

This document, version 2 of the GREI Metadata Recommendations, provides updated guidelines for generalist data repositories to standardize their metadata using the DataCite Metadata Schema 4.6 and based on community feedback received on version 1. The goal is to enhance the interoperability and discoverability of datasets, particularly those from NIH-funded projects. The recommendations strongly encourage repositories to collect a specific subset of metadata properties and use designated vocabularies and values to support common use cases for sharing, discovering, and tracking the impact of data.

2. Harvard Longwood Research Data Management Newsletter

Looking for another research data management newsletter to subscribe to? Harvard Longwood Research Data Management has a quarterly newsletter. It contains announcements and events particular to the Harvard community, but also includes more information more broadly applicable such as tips and links to helpful resources and articles. Past quarterly newsletters are shared on the site.

3. Open Accessibility in Education Research: Enhancing the Credibility, Equity, Impact, and Efficiency of Research

Openness is a foundational principle in science. Making the tools and products of scientific research openly accessible advances core aims and values of education researchers, such as the credibility, equity, impact, and efficiency of research. The digital revolution has expanded opportunities for providing greater access to research. In this article, the authors examine three open-science practices—open data and code, open materials, and open access—that education researchers can use to increase accessibility to the tools and products of research in the field. For each open-science practice, they discuss what the practice is and how it works, its primary benefits, some important limitations and challenges, and two thorny issues.

4. Ten Simple Rules for Creating a Good Data Management Plan

A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). Here, the author presents ten simple rules that can help guide the process of creating an effective plan for managing research data—the basis for the project’s findings, research papers, and data products. The paper focuses on the principles and practices that will result in a DMP that can be easily understood by others and put to use by your research team. Moreover, following the ten simple rules will help ensure that your data are safe and sharable and that your project maximizes the funder’s return on investment.

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.

• Westat - Clinical Data Manager (Hybrid)

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.

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.

Buy Me A Coffee

Welcome to Issue 47 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. How to Get Started Using AI with R

There are so many ways to use AI with R now that it’s genuinely hard to know where to start. Should you just use ChatGPT in a browser tab? Install something into your code editor? And if so, which one? In this video, David Keyes walks through the main options, with the pros and cons of each.

2. 2026 UC Open Research Day Materials

This repository contains materials from the University of Cincinnati’s Open Research Day. Presentation slide decks include topics such as “Beyond Data Available Upon Request: Closing the Gap in Open Data”, “Open by Design: Scaling and Sustaining Open at CMU”, “Science Communication: Sharing Your Research with the Public”, and “Hitting the Open Road: A Roadmap for Community Centered Dissemination Planning”, as well as slides for several lightning talks.

3. Lab Handbook Template

Research labs routinely face challenges related to onboarding, role clarity, expectations, and continuity. This template, from University of
Illinois Urbana-Champaign, provides a starting place for consistently communicating expectations to all lab members. Drawing on an extensive review of existing lab handbooks and related literature, the template emphasizes policies and expectations rather than task-specific procedures and adopts a modular, “à la carte” structure that can be adapted to diverse lab contexts.

4. Community-Driven Guidance: Help Shape Trustworthy Data Repository Characteristics for Canada

The Digital Research Alliance of Canada’s Data Repositories Expert Group (DREG) has struck a working group to develop guidance to help researchers select data repositories in fulfillment of the Tri-Agency Research Data Management Policy data deposit requirement, anticipated to take effect in early 2027. The working group is conducting a literature review to identify characteristics across technical, operational, and organizational dimensions — including persistent identifiers, preservation, metadata, cybersecurity, data handling ethics, and Indigenous community engagement — with particular attention to Canadian priorities such as data sovereignty, bilingualism, and Tri-Agency alignment. Findings will inform a guidance document and complementary decision-making resources. On Wednesday, June 24 from 1:00–2:30 PM ET, they are hosting a 90-minute community consultation webinar to share preliminary results from the literature review and gather attendee input on the characteristics identified.

5. Test-Driven Data Analysis

This book is for anyone working with data who is interested in producing more robust and reliable results. The goal of this book is to reduce the frequency and severity of errors in all forms of data processing and analysis, from the simplest query to the most complex machine-learning data pipeline. Test-driven data analysis (TDDA) is both a methodology for improving data quality and data pipelines, and a set of software tools—the Python tdda library—for helping to implement that methodology. The library is written in Python and presents a Python API, so is most relevant to people using Python, but it also provides a command-line interface. The author will be making the entire book available for free, releasing a chapter a week over 16 weeks. Chapters 1 and 2 are available now.

6. How to Cite Data Resources in Wikipedia

A data citation is a short, formatted description that provides the information necessary to retrieve a published dataset. Consistent and reliable citations are an important part of making research open and reusable. In this blog post, the DRI Labs team provides guidance on how metadata describing Repository datasets can be used to generate a well-formatted Wikipedia citation.

7. Skills for the Curation of Sensitive Data

This report, from Health Data Research UK, provides an evidence-based snapshot of current skills gaps, training provision, and barriers in sensitive data curation. It draws on surveys, focus groups, and interviews with curators, TRE/SDE/Safe Haven operators, governance specialists, and training providers across multiple sectors and data types. The accompanying recommendations for key stakeholders set out a roadmap to improve skills and strengthen recognition in sensitive data curation.

Oldies but Goodies

Older resources that are still helpful

1. On the Reuse of Scientific Data

While science policy promotes data sharing and open data, these are not ends in themselves. Arguments for data sharing are to reproduce research, to make public assets available to the public, to leverage investments in research, and to advance research and innovation. To achieve these expected benefits of data sharing, data must actually be reused by others. Data sharing practices, especially motivations and incentives, have received far more study than has data reuse, perhaps because of the array of contested concepts on which reuse rests and the disparate contexts in which it occurs. In this essay the authors explicate concepts of data, sharing, and open data as a means to examine data reuse. They explore distinctions between use and reuse of data. Lastly they propose six research questions on data reuse worthy of pursuit by the community.

2. Open with Care: Indigenous Researchers and Communities are Reshaping How Western Science Thinks About Data Ownership

This 2024 Science article follows UC Berkeley Ph.D. student Leslie “Leke” Hutchins, a Native Hawaiian researcher who redacted species names and location data from his arthropod study after determining the information was culturally sensitive to the Indigenous farmers on whose land it was collected. The article explores the broader tension between academia's push for open data and the growing Indigenous data sovereignty movement, guided by the CARE principles, which hold that Indigenous communities should have authority over how data about their lands, cultures, and bodies are collected and used. Institutions like NIH, NSF, and major publishers are beginning to adapt their policies in response, though Indigenous principles remain largely optional while open data mandates are increasingly required.

3. Creating Unique Participant Study Identifiers

Assigning unique participant identifiers help protect participant privacy and confidentiality. Research teams use a variety of approaches to generate these identifiers, each with its own tradeoffs. This post reviews common methods and concludes with best practice recommendations.

4. Cornell Data Services Glossary

This glossary provides definitions for common research data management terms. This is not meant to be a comprehensive list.

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.

• Tennessee State Government - OEI Data Governance Officer

• Boston College - Data Services Librarian

• Education Data Center - Data Analyst

• University of Colorado - Research Scientist / Data Manager and Analyst (Remote) - Job Number: 40088

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.

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.

Buy Me A Coffee

Welcome to Issue 46 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. Resources to Meet the New NIH Data Sharing Plan Format

This year the NIH released a format update to it’s Data Management and Sharing Plan that is required for plans submitted after May 25, 2026 (see NOT-OD-26-046 for details). As they did in 2023, a group of data librarians came together to create implementation resources for researchers, librarians, and other research support professionals. They hope that these will help you as you work to comply with this change. Materials include an Annotated Example Plan, Mapping the 2023 Plan to the 2026 Update, Guidance on Repository Selection (including a very nice Interactive Decision Tool), and Materials for Librarians, including a PowerPoint slide deck, a checklist to give to researchers, and a list of updated LibGuides.

2. Beyond Vibe Coding: Responsible AI-Assisted Data Analysis for Reproducible Research - Workshop

Generative AI is not only significantly affecting the world of software development, but also rapidly changing the way research is conducted. This free online workshop, happening Friday June 5th at 1pm (CET), offers a structured opportunity to explore both the practical use of AI-based coding tools for data analysis and the broader questions they raise for scientific integrity and responsibility. By the end of the workshop, you will be equipped to make informed and conscious decisions about how to choose and use AI-based coding assistants in your own research.

3. Platformization of Research Data Infrastructure: A Decadal Analysis of Scientific Data Management Plans

New federal research policies support data-driven science by requiring data management planning and data preservation for open access to scientific data and research products. This article shows a “platform effect” in how scientists plan for data preservation. By examining 976 data management plans for projects funded between 2011 and 2021 by the United States’ National Science Foundation (NSF) in five research areas, the authors document a shift in planning for data preservation on corporate-owned platforms away from open-source solutions. These findings raise questions about the potential impact of cloud storage services and corporate platforms on scientific knowledge production. The authors argue that the dynamics of platform capitalism and open access mandates create new challenges for scientists, policymakers, and proponents of open science because of how value and control are distributed within digital preservation infrastructures. As research data increasingly becomes managed and made accessible by platforms and cloud storage services, scientific knowledge risks becoming enclosed, with implications for the reconfiguration of knowledge infrastructure, control over long-term preservation, open access to data, and the circulation of scientific research.

4. Post45 Data Collective Tabular Data Style Guide

This Style Guide is a cheatsheet for how to present and submit your tabular data to P45DC. The guide provides support at multiple points throughout your data journey, whether you are actively making data decisions or have already collected, curated, and documented your data. Though the guidelines are targeted to the latter-end of the dataset lifecycle (specifically storage, publication, and reusability), they will also be helpful for making critical decisions well in advance of submitting to P45DC. The authors hope this guide will also be a useful general-purpose reference for anyone working with tabular humanities data — whether or not you are submitting to P45DC — including researchers, students, librarians, and instructors thinking through how to clean, structure, and document a dataset.

5. 2026 Roadmap to Becoming a Data Analyst

In this 5 part series, Brien Grant takes readers through a series of brief tutorials on skills that are helpful for data analysts. Part 1 walks you through useful Excel skills, Part 2 goes through dashboarding and visualization, Part 3 reviews SQL functions, Part 4 covers Python basics, and then Part 5 discusses the importance of domain knowledge.

6. Enabling the Reuse of Personal Data in Research: A Classification Model for Legal Compliance

Inspired by a proposal made almost 10 years ago, this paper presents a model for classifying personal data to inform researchers on how to manage it. The classification is based on the principles of the European General Data Protection Regulation (GDPR) and its implementation under Spanish law. The paper also describes the conditions in which personal data should be stored and accessed to ensure compliance with data protection regulations and to safeguard privacy. The authors developed the work collaboratively with the library (CRAI) and the Data Protection Office of the University of Barcelona. The outcomes of this collaboration were a decision tree for researchers and a list of requirements for research data repositories to ensure personal data is stored and accessed securely. This proposal is aligned with the FAIR principles and the commitment to responsible open science practices.

7. Data Management for Beginners - Workshop

New researchers are often blindsided by data cleaning. In research methods courses you work with tidy datasets, so it’s easy to assume you collect data one day and analyse it the next. In reality, poor naming conventions, open-ended responses, and missing documentation can mean weeks of tedious, manual, error-prone work standing between you and your results. Join Reveal Research on Friday June 12th 12pm-1pm (AEST), for this free online workshop in which Dr Jenny Richmond walks you through how to set up your project so your data is clean from day one.

Oldies but Goodies

Older resources that are still helpful

1. Checklist for Universities on Implementing the UNESCO Recommendation on Open Science

This document is part of the UNESCO Open Science Toolkit, designed to support implementation of the UNESCO Recommendation on Open Science. It was prepared in partnership with Library Support for Embedded NREN Services and E-infrastructure (LIBSENSE). The aim is to provide practical assistance to the university community to better understand the Recommendation on Open Science, in particular by highlighting the areas that apply to university leaders who wish to support its implementation.

2. FAIR Sharing for Education Researchers: Introducing LDbase Community Metadata Records on OSF - Recording

This webinar introduces a new OSF feature that makes it possible and easy for education researchers to make their research FAIR by annotating their OSF projects with specialized metadata. Experts from LDbase, whose custom metadata template is for projects in the fields of education, learning, and human development, introduce their template and demonstrate how its use promotes FAIR sharing in education research.

3. The Views, Perspectives, and Experiences of Academic Researchers with Data Sharing and Reuse: A Meta-Synthesis

Funding agencies and research journals are increasingly demanding that researchers share their data in public repositories. Despite these requirements, researchers still withhold data, refuse to share, and deposit data that lacks annotation. The authors conducted a meta-synthesis to examine the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data. The four major themes that emerged were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Researchers lack time, resources, and skills to effectively share their data in public repositories. Data quality is affected by this, along with subjective decisions around what is considered to be worth sharing. Deficits in infrastructure also impede the availability of research data. Incentives for sharing data are lacking.

4. Ten Principles to Improve Dataset Discoverability

The FAIR (meta) data principles provide overarching guidelines to make metadata and data Findable, Accessible, Interoperable and Reusable. While significant effort has been dedicated to specific recommendations that enable best practices in implementing FAIR principles, particularly from a data curation perspective, this document focuses primarily on enhancing the discoverability of data from the perspectives of data searchers and data users, including both human users and machine-based systems. The document outlines ten principles, along with practical implementation recommendations for each principle, for improving dataset discoverability. The ten principles and recommendations are not confined to a singular community. Instead, they are applicable across a spectrum of communities since providing a data discovery service and good data discovery experience involves a shared responsibility among various stakeholders.

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.

• Leiden University Medical Center - Data Management Specialist

• Boston College - Data Services Librarian

• DARIAH-EU - Data Steward

• MIT Department of Brain and Cognitive Sciences - Neuroscience Research Methodologist

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.

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.

Buy Me A Coffee

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.

• Chapin Hall - Associate Researcher (Remote)

• Data Rescue Project - Metadata Analyst (Part-time - Remote)

• Pew Charitable Trusts - Associate I, Research Quality and Support

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.

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.

Buy Me A Coffee

Welcome to Issue 44 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. psychds - R Package

psychds provides tools for creating, editing, and validating psychological research datasets following the Psych-DS standard. The package includes an interactive Shiny application for building Psych-DS compliant datasets from existing directories, functions for validating existing datasets against the standard, tools for generating data dictionaries with professional formatting, and OSF integration for publishing validated datasets.

2. Mind the Gap: A Call for Institutional Investment and Support to Prevent Data Sharing Requirements From Causing Harm

While requiring more public data sharing has admirable goals and great potential benefits, a lack of accompanying support to appropriately assess data before its release undermines the ethics and positive contributions to public-researcher relationships that data sharing is meant to bolster. This article discusses how this gap should be addressed before the impact of easily-anticipated harms becomes undeniable. While research data professionals are likely to be a critical part of the advocacy and collaboration needed to address the issue, the larger-scale investment of resources by institutions in support and education for how to ethically meet these requirements is needed. Without additional support, neither individual researchers nor data professionals will be able to adequately mind this gap.

3. READMEBuilder

REAMEBuilder is a Shiny application that walks researchers through building a high-quality README file for research datasets and code. It automatically describes tabular data columns (ranges, levels, NAs), detects R package dependencies from scripts, supports script run-order documentation, and exports a formatted README.md.

4. Data Curation as Data Literacy Education: Grad’s Declassified Data Survival Guide

To support graduate students in managing their research projects through temporal gaps, the authors created a research data management guide for graduate students, providing easily implementable practices that can be integrated into projects and workflows with as minimal burden as possible to the student or interruption to their team. This quick-start guide is designed to teach graduate researchers data literacy through the curation of their own project materials. In other words, by learning curation best practices and integrating them into an existing research project—where they have a vested interest in seeing the project succeed—graduate students can also become comfortable with how to evaluate data for use in their future research, how to set up projects from the beginning to plan for starts and stops in the work, and how to effectively manage and describe project materials.

5. European Open Science Resources Registry

In the European Open Science Resources Registry, you can find a curated collection of key resources supporting Open Science across Europe, including national policies, strategies, best practices, templates, and impact stories. Users can easily filter resources by country, date, topic, and document type, making it simple to locate relevant information. Policies and strategies are vital to creating a collaborative, transparent, and accessible research environment, and the registry goes beyond simply listing these documents. You’ll find detailed information on each policy, including the option to read full texts where available, helping you understand their scope, implementation, and impact in depth.

6. Your Field Notes are Valuable Research Data - So Share Them

When conducting research fieldwork we generally produce field notes, often as the primary data source or as contextualisation of other gathered data such as interviews. In this post, Pascal Flohr argues that these are an important part of the research dataset and should be shared as openly as possible, and offers things to take into account when deciding to share field notes.

7. A Practical Guide to User-Friendly Data in Publications

This guide from the European Data Portal will help you with all of the data-related aspects of your publication: writing about data, creating data visualisations, and preparing and publishing data. It will provide you with tips and resources that will make the data in your publication more accessible and reusable.

Oldies but Goodies

Older resources that are still helpful

1. Cleaning Excel Data Using R

Excel spreadsheets come in all shapes and sizes. In this 30 minute video, Lee Durbin shows you how to use three packages in R (tidyverse, lubridate, and tidyxl), to whip your spreadsheet into shape in no time.

2. Collection of Open Science Integrity Guides - Citations to Retracted Papers

References to retracted publications can pose a reliability issue in scientific literature since retractions indicate that a publication has been found unreliable. Citations/references to such articles can disseminate unreliable information in the scientific literature. It is stated in Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals that authors are responsible for checking that none of the references cite retracted articles except in the context of referring to the retraction. However, not all retracted articles are cited after their retraction (post-retraction citations), and not all citations serve the same purpose. It is important to consider the context in which a retracted article is cited. A retraction in the references does not automatically make all citing articles unreliable, but citations to retracted publications can serve as a fingerprint for identifying potential issues in the literature. This guide provides information about how to identify such citations and what nuances should be considered in the analysis and reporting of these cases.

3. Data Journals: Incentivizing Data Access and Documentation Within the Scholarly Communication System

Data journals provide strong incentives for data creators to verify, document and disseminate their data. They also bring data access and documentation into the mainstream of scholarly communication, rewarding data creators through existing mechanisms of peer-reviewed publication and citation tracking. These same advantages are not generally associated with data repositories, or with conventional journals’ data-sharing mandates. This article describes the unique advantages of data journals. It also examines the data journal landscape, presenting the characteristics of 13 data journals in the fields of biology, environmental science, chemistry, medicine and health sciences. These journals vary considerably in size, scope, publisher characteristics, length of data reports, data hosting policies, time from submission to first decision, article processing charges, bibliographic index coverage and citation impact. They are similar, however, in their peer review criteria, their open access license terms and the characteristics of their editorial boards.

4. tidylog - R package

Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins. See this vignette for more information.

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.

• Research Data Scotland - Variety of openings

• TU Delft - 4TU.ResearchData - Data Curator

• Research Data Alliance - Project Officer (Remote)

• Princeton University - Research Specialist

• TU Delft - Data Steward (maternity cover)

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.

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.

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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.

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.

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Welcome to Issue 42 of the RDM Weekly Newsletter!

Some exciting news! RDM Weekly has its first sponsor! As part of their commitment to developing Open Educational Resources, the NIH Data Management for Data Sharing Workshop project is now a supporter of this 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. Sharing Digital Health Data Responsibly: Balancing Open Science with Participant Privacy

Data sharing fuels scientific discovery, but new evidence shows that certain types of digital health data (i.e., raw motion signals from wearable sensors) can reveal more about individuals than expected. Research now demonstrates that even a few steps of walking can uniquely identify someone, raising important privacy concerns. In this article, the authors argue for policies and education that both enable open science and safeguard research participants. The authors provide a multidisciplinary perspective, spanning research ethics, data science, and computational health, on how to share data responsibly in the era of digital health.

2. Getting Started with Claude Code: A Researcher’s Setup Guide

This article is Part 1 of a series on AI coding tools for empirical research. These blog posts are companion pieces to go with a Markus Academy video series (which you can view in the post). This post reviews what is possible with these tools, what Claude Code is, a description of the various ways people use these tools, how to get started using Claude Code, as well as considerations if you are working with sensitive data.

3. Updated NIH Data Management and Sharing Plan Now Available in the DMP Tool

The new 2026 Data Management and Sharing Plan (DMSP) format for NIH is now available in the DMP Tool. While this form is not required for applications until May 25th, NIH has stated that they are already accepting it and encourage people writing new plans to use the new format. This blog posts provides guidance on using the new template and also discusses any templates that will be deprecated moving forward.

4. A Crisis of Unverifiable Data: IP Analyses May Be Key to Ensuring Data Integrity in Online Survey Research

Online survey platforms are widely used across disciplines, yet researchers have limited ability to verify who participates in their studies. Because fraud is ever evolving, the authors argue that researchers’ willingness to entrust even the most reputable platforms with responsibility for data integrity raises the specter of a crisis of unverifiable data. The authors illustrate this threat by documenting data irregularities on Prolific Academic, a platform consistently regarded as more reliable than its competitors. Using IP analyses, Retrospective Study 1 (N=4,225) reveals a rapid increase in five indicators of potential fraud in Prolific data collected in Fall 2024–Winter 2025 – with up to ~40% flagged as suspicious. These issues, however, remained undetected by five established best practices for data integrity and quality. Retrospective Study 2 (N=4,624) shows that while these issues are not unique to Prolific, data integrity there was significantly lower than on CloudResearch Connect and Qualtrics Edge Panels during Winter 2024–Spring 2025. Together, these findings suggest that without IP analyses, a core assumption of empirical psychology – that samples draw upon their stated population of interest – can silently fail in online survey research. To advance data integrity controls, the authors introduce two IP-based tools: the ip2location.io R package for auditing data integrity in past studies; and a real-time IP filter for blocking suspicious participants in future studies, whose effectiveness they test in Prospective Study 3 (N=328).

5. Got Bugs? Here’s How to Catch the Errors in Your Scientific Software

Science is becoming increasingly computational. Experimental data must be logged, cleaned, checked and analysed. Data analysis often involves iterative trial and error using ‘scripting’ programming languages such as Python and R. The outputs of such programs are then included in papers, presentations and grant applications. In this article, computer scientists share their advice for ensuring that your scientific software does what it’s supposed to do.

6. Scientific Datasets are Riddled with Copy-Paste Errors

In this paper, Markus Englund reviews cases where copy-paste errors have been detected using a piece of software he has built. Of the 600 publicly available datasets he and a volunteer team have reviewed, 18 cases were serious enough to raise concerns, and the 3 covered in the post were the most exciting ones. You can also read about this work in this Retraction Watch article.

7. Using Synthetic Data in Communication Sciences and Disorders to Promote Computational Reproducibility and Transparency

Reproducibility is a core principle of science, and access to a study’s data is essential to reproduce its findings. However, data sharing is uncommon in the discipline of communication sciences and disorders (CSD), often due to concerns related to privacy and disclosure risks. Synthetic data offer a potential solution to this barrier by generating artificial data sets that do not represent real individuals yet retain statistical properties and relationships from the original data. This study aimed to explore the feasibility and preliminary utility of synthetic data to promote transparency and reproducibility in the discipline of CSD. Ten open data sets were obtained from previously published research within the American Speech-Language-Hearing Association “Big Nine” domains (articulation, cognition, communication, fluency, hearing, language, social communication, voice and resonance, and swallowing) across a range of study outcomes and designs. Synthetic data sets were generated with the synthpop R package. Findings suggest that synthetic data can effectively maintain statistical properties and relationships across a wide range of nonhierarchical data commonly seen in the discipline of CSD. The published version of this paper can be found here.

Oldies but Goodies

Older resources that are still helpful

1. The Ghosts in the Data

Explicit knowledge is anything that you can read about, knowledge that’s easy to share and pass on. Implicit knowledge is knowledge that people gain by context that’s very hard to pull out consciously. In this 2021 post, Vicki Boykis explored several examples of implicit or “ghost knowledge” that she has observed in her own data career.

2. A Comparison of Packages to Generate Codebooks in R

Codebooks provide descriptive, variable-level information as well as univariate summary statistics which allows users to understand the contents of a dataset without ever opening it. This link includes materials from a 5 minute lightning talk given to R-Ladies NYC in 2022. The topic was comparing four R packages that can be used to create codebooks to document your data.

3. CESSDA Data Management Expert Guide

The DMEG is designed by European experts to help social science researchers make their research data Findable, Accessible, Interoperable and Reusable (FAIR). You will be guided by different European experts who are - on a daily basis - busy ensuring long-term access to valuable social science datasets, available for discovery and reuse at one of the CESSDA social science data archives.

4. 7 Steps towards Reproducible Research

This Carpentries workshop aims to take you further down your reproducibility path by providing concepts and tools you can use in your everyday workflows. This course will examine how Reproducible Research builds business continuity into your research group, how the culture in your institute ecosystem can affect Reproducibility, and how you can identify and address risks to your knowledge.

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.

• University Of Cincinnati Libraries - Qualitative Data Services Librarian

• University of Rochester - Sr Clinical Research Data Specialist (Part-time - Remote)

• Leiden University - Specialist Research Data Management (Posting in Dutch)

• Research Institute of the McGill University Health Centre - Data Manager (Hybrid)

• Washington University in St. Louis - Research Support Librarian (Hybrid)

• University of Cambridge - Open Access Service Manager

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.

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.

Buy Me A Coffee

Welcome to Issue 41 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. NSF Data Management and Sharing Plan Online Portal Overview

Effective April 27, 2026, the National Science Foundation Data Management & Sharing Plan (DMSP) will be updated to reflect supplemental policy notice NSF 26-202 published on January 22, 2026. Going forward proposals must include a DMSP created using the tool implemented on Research.gov. You can review the online DMSP sections and descriptions in the shared link above.

2. Rethinking Offboarding in Research: From Administration to Data and Knowledge Transfer

This presentation introduces a structured approach to offboarding in research, focusing on the transfer of data, code, and knowledge when project members leave. Using practical scenarios, it highlights common risks such as missing documentation, unclear responsibilities, and limited reproducibility. It further demonstrates how these challenges can be addressed through simple templates and integrated research data management practices.

3. It Depends - A Comical Data Steward Replacement

Questions related to data management rarely produce one universal answer. The applicable response depends on context, discipline, infrastructure, data aggregation level, applicable regulation, contracts, and institutional policy. This tool is a comic reminder that when handling research data, we all benefit from learning the regulatory frameworks and standards that apply to research projects, because it always depends. Please contact your data steward, legal support, or RDM team for real advice.

4. Draft UKRI Research Data Policy

While this policy is specifically for individuals and organisations applying for funding or funded by UKRI, this draft may provide an example for others wanting to develop a similar policy for their group, organization, or work. This policy sets out UK Research and Innovation’s (UKRI) expectations for sharing and managing research data and other research-relevant digital objects, including metadata, algorithms, software, code and workflows. You can read more on the background of the draft policy here.

5. Scrapers, Pipelines, and AI, Oh My! Tools to Accelerate Data Rescue - Workshop

One of the questions the Data Rescue Project hears regularly is if folks can still help rescue data without advanced technical skills. The answer is a resounding YES! But, if you’re interested in learning more about how you could scrape websites to capture a large amount of data quickly, join them on April 29, from 4:30-5:30pm Eastern for a hands-on workshop. Two volunteers on the project (Mike Kraley, Sef Kloninger) will demo tools they’ve been developing to help scrape government websites.

6. How to Share Your Research Code Openly: Best Practices for Transparency and Reuse

Openly sharing the code that underpins your research makes your results transparent, improves reproducibility and replicability of your findings, can increase citations and visibility, and helps you meet funder, institution, and publisher requirements. Building on a previous introduction to open code sharing, this Springer Nature practical guide shows you how to do it responsibly for maximum reward. Springer Nature also recently released another helpful guide, “How to share your research protocols and methods openly: Best practices for transparency and reuse”. Building on a previous introduction to sharing protocols, this guide covers how to prepare, publish, license and cite your protocols so they’re easy to find, use, and receive credit for.

7. RC-SFA Data Management Templates and Guidance for Standardized, Reusable AI-Ready Data Packages

This data package provides templates and supporting documentation developed by the River Corridor Science Focus Area (RC-SFA) to communicate its approach to managing and publishing AI-ready data. The package is intended to help data users and data producers understand the structures, metadata practices, and quality-control approaches that support consistent, reusable, and machine-actionable data products across RC-SFA studies. Rather than focusing on a single experimental dataset, this package documents the data management framework used to make RC-SFA data easier to find, ingest, navigate, and interpret. The materials in this package reflect RC-SFA practices for standardized data package organization, including the use of a human- and machine-readable README, file-level metadata, data dictionaries, descriptive file naming, method identifiers, and automated and review-based quality assurance procedures. Together, these components illustrate how RC-SFA extends FAIR data principles toward AI-readiness by prioritizing deep metadata, consistency across data packages, and support for informed downstream reuse by both humans and computational tools.

Oldies but Goodies

Older resources that are still helpful

1. SQL Murder Mystery

There's been a Murder in SQL City! The SQL Murder Mystery is designed to be both a self-directed lesson to learn SQL concepts and commands and a fun game for experienced SQL users to solve an intriguing crime. This exercise is meant more as a way to practice SQL skills than a full tutorial. If you've never used SQL at all, try the walkthrough. See the GitHub repository for more information about this SQL Murder Mystery.

2. R Environments, Scoping, and Evaluation with Some Applications

This paper aims to expose one of R 's important features that seems to be little‐known by most users. The particular feature is language scoping, which R inherits from languages like Algol60 and Scheme . Although the feature is comparatively little known, it is quite important, and knowing about it can change the way that R is used. Rather than proceeding as a technical exposition, the content is presented as a series of examples. These are formatted (without continuation prompts) so that they are easy to copy and paste. The right way to read the paper is to copy the examples and experiment with them.

3. How to…Write a Lab Handbook

Samuel Mehr has studied what makes a good lab handbook so you don’t have to – and says a good one could improve your team’s science and save time. This article includes a list of common topics to include in a lab handbook and also reviews why spending the time and energy to create one can pay off.

4. Datasheets for Datasets

The machine learning community has no standardized way to document how and why a dataset was created, what information it contains, what tasks it should and should not be used for, and whether it might raise any ethical or legal concerns. To address this gap, the authors of this paper propose the concept of datasheets for datasets. In the electronics industry, it is standard to accompany every component with a datasheet providing standard operating characteristics, test results, recommended usage, and other information. Similarly, the authors recommend that every dataset be accompanied with a datasheet documenting its creation, composition, intended uses, maintenance, and other properties. Datasheets for datasets will facilitate better communication between dataset creators and users, and encourage the machine learning community to prioritize transparency and accountability.

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.

• Redivis - Research Advocate and Support Engineer (Remote)

• UP Parternship - Data Manager

• Harvard T.H. Chan School of Public Health - Research Data and Project Manager

• University of Delaware - Research Data Management Librarian

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.

Buy Me A Coffee