RDM Weekly - Issue 048
A weekly roundup of Research Data Management resources.
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.
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.