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.
Just for Fun
Sponsor
This newsletter is supported in part by the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health under Award Number R25HD114368. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health. Read more about the NIH Data Management for Data Sharing Workshop Project.
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