RDM Weekly - Issue 055
A weekly roundup of Research Data Management resources.
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.
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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