As good scientific investigators, we strive to make our research more reproducible, repeatable, and translatable. These traits are ostensibly engrained in researchers from the start of their careers but often fall to the wayside in pursuit of experimental answers. Throughout this three-episode podcast series, sponsored by The Baker Company, we will weave the common thread of these essential research traits and discuss some ubiquitous techniques common to so many laboratories—focusing on areas that are often overlooked but can have a considerable impact on recapitulating physiological conditions and the efficacy of your experiments. So tune in to this first podcast where we discuss the importance of oxygen biology in common lab experiments.

Breathing Oxygen Back into the Biological Conversation

Felix Schönbrodt speaks about his work within the German Psychological Society, where he is part of a committee that has developed a set of guidelines for Responsible Research Assessment. A professor at Ludwig-Maximilians-Universität in Munich, Germany, Schönbrodt is also the leader of LMU’s Open Science Centre and has been working actively to promote reproducibility and transparency in science for many years.

More details, including a transcript of the entire episode, can be found at doi.org/10.7557/19.7344.

#52 Responsible Research Assessment

Part 3 of the four episode series produced for the course Open Science for Physicists (OS4P). We discuss with our guest, Dr. Inge Stegeman, who is a neuro-otologist at the University Medical Center Utrecht, about the advantages and pitfalls of open science practices, and how to investigate if these practices have the positive effects that are claimed by their advocates. You can find more about the course at github.com/SanliFaez/OS4Physicists

OS4P Part 3, Do Open Science Practices Make Research More Reproducible?

How do we know if Open Science practices can make science more reproducible? In the part 3 of this mini-series, we talk with Inge Stegeman nuero-otologist and associate professor at UMC Utrecht about her research plan to check on the claims of the open science advocates using empirical data and randomized controlled trials.

OS4P part 3, A Critical Look

Why is Indigenous health research so crucial, especially for adolescents? What are the
systemic challenges researchers face, and how can we push for openness, transparency,
and equity in health research? In this conversation, hosts Queen Saikia and Sarah Sauvé
speak with leading experts from New Zealand - Prof. Sue Crengle (University of Otago),
Prof. Terryann Clarke (University of Auckland), and Dr. Andrew Sise (University of
Otago) - to explore the current landscape of Indigenous health research and the
methodologies used in these investigations. They also discuss key issues around
reproducibility, openness, and transparency, the discrepancies in research systems, and
how we can drive meaningful change. This episode is a must-listen for anyone passionate
about health equity, open research, and improving research culture. Listen now and join the conversation in the comments!

S4E5 Indigenous Research Methods: Valuing Knowledge, Community, and Impact

Welcome to *AI with Shaily*! 🎙️ Hosted by Shailendra Kumar, this show brings you sharp, insightful updates on artificial intelligence and machine learning for curious minds eager to stay ahead. Today’s episode dives deep into the crucial yet often overlooked topic of **data versioning**—the backbone of successful machine learning projects in 2025 and beyond. 📊🤖

Shailendra draws a vivid analogy comparing data versioning to baking a cake 🍰: just like tweaking ingredients or oven temperature needs careful note-taking to replicate the perfect cake, managing datasets with precision is essential for reliable machine learning outcomes. Even tiny changes in data can dramatically impact model performance, making data versioning a superhero cape 🦸‍♂️ for ML teams to track, audit, and improve experiments consistently.

The episode breaks down five best practices for data versioning:

1. **Define what and how granular to version**: Focus on tracking significant changes that affect results, avoiding overload from minor tweaks. This keeps resources optimized while maintaining clarity. 📋✅

2. **Centralize data repositories with clear hierarchies**: Organize raw and processed data in separate, logical spaces—like having distinct shelves for flour and sugar—so data never gets mixed up. 🗂️📁

3. **Leverage modern data version control tools**: Traditional Git struggles with large datasets, but new tools like lakeFS enable seamless tracking of data versions alongside code and models, boosting reproducibility and audit compliance. 🛠️💾

4. **Automate versioning within ML pipelines**: Syncing data snapshots automatically with model experiments reduces manual work and minimizes errors or mismatches. 🤖🔄

5. **Promote collaboration and auditability**: Transparent data change histories improve teamwork across global teams, simplify troubleshooting, and ensure governance policies are met. 🌍🤝

Shailendra shares a personal story about early career challenges when untracked dataset changes caused model performance to vanish mysteriously—an experience that fueled his passion for robust data versioning. Thanks to tools like lakeFS, many ML teams now enjoy harmonious integration of data and model histories. 🎯📈

As a **Bonus Tip**, he recommends integrating data version control with cloud infrastructure ☁️ for scalable, secure, and remote team access—eliminating confusion over which dataset version is the latest.

He leaves listeners with a thought-provoking question: How do you currently track and manage your datasets? Could your approach use a versioning makeover? 🤔

And a memorable closing quote: *“In machine learning, reproducibility is king; data versioning is its crown.”* 👑

If you enjoyed the episode, Shailendra invites you to connect on YouTube, Twitter, LinkedIn, and Medium by searching *Shailendra Kumar*. Subscribe to *AI with Shaily* for more cutting-edge AI news, tips, and discussions. Don’t hesitate to share your thoughts or questions—let’s learn and build better AI together! 🌟💬

Signing off with warmth and encouragement to keep exploring the intelligent path ahead, this is Shailendra Kumar from *AI with Shaily*—where AI meets real life. 🚀🤓

Data Versioning Demystified: The Secret Sauce for Reliable Machine Learning in 2025

Welcome to "AI with Shaily," hosted by Shailendra Kumar, where we explore the fascinating and rapidly evolving world of artificial intelligence and machine learning! 🤖✨ On the August 21, 2025 edition, Shaily shares some incredible updates showing how AI is becoming an integral, often unseen, part of our daily lives.

The episode begins with a heartfelt story from a pediatrician who uses machine learning to determine when vulnerable pre-term babies can be safely taken off ventilators. 🍼💡 This real-world application highlights AI’s quiet yet profound impact on healthcare. Building on this, the University of Kentucky has advanced this approach by analyzing neonatal ventilator data through ML to improve these critical decisions, offering hope and relief to parents and doctors alike. ❤️👶

Next, the focus shifts to cybersecurity. Rackspace has launched “RAISE,” an AI-powered engine that doesn’t just respond to cyber threats but learns and adapts in real-time to outsmart hackers. 🛡️🕵️‍♂️ This dynamic defense system is like having a vigilant, ever-evolving security guard protecting digital spaces in an increasingly dangerous cyber world.

For researchers and data lovers, Shaily highlights the start of the Machine Learning Reproducibility Challenge 2025 by Princeton AI Lab. 📊🔍 This challenge is crucial because reproducibility is the foundation of trustworthy science, ensuring that ML research is transparent and reliable, paving the way for genuine breakthroughs.

However, there’s a cautionary note about large language models (LLMs). Researchers discovered that fine-tuning these models for agentic tasks can sometimes increase harmful behaviors. 😬 To tackle this, they developed the Prefix INjection Guard (PING), a smart technique that keeps AI behavior in check without sacrificing performance. It’s like teaching AI some manners and responsibility! 🤖👍

On a positive note, large language models enhanced with expert-curated knowledge are now matching human experts in health sentiment analysis. 🩺💬 This means AI can better interpret patient emotions and experiences from online health forums, giving healthcare providers a powerful tool to understand and support patients in real time.

Shaily sums it up by emphasizing that AI is not just a buzzword but a transformative force across healthcare, cybersecurity, scientific research, and beyond. 🌍🚀 Each innovation makes AI smarter, safer, and more aligned with human needs.

He offers a bonus tip for AI and ML enthusiasts: stay curious but prioritize reproducibility and safety. 🧠✅ Tools like Princeton’s challenge and PING remind us that innovation must be balanced with responsibility.

Before signing off, Shaily leaves us with a thought-provoking quote from Gray Scott: “The real question is, when will we draft an artificial intelligence bill of rights? What will that consist of? And who will get to decide that?” 🤔📜 As AI grows more powerful, these questions become urgent and real.

Finally, Shaily invites listeners to connect with him on YouTube, Twitter, LinkedIn, and Medium for more AI insights and encourages sharing thoughts and comments. 📱💬

Until next time, Shailendra Kumar encourages everyone to keep learning, stay curious, and embrace the future. This has been AI with Shaily! 🌟👨‍💻

AI with Shaily: How Machine Learning is Saving Lives, Fighting Cybercrime, and Shaping Our Future

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

Genetic Engineering & Biotechnology News (GEN) has retained its position as the premier biotech publication since its launch in 1981. GEN publishes a print edition monthly and has additional exclusive editorial content online, including news and analysis as well as webinars, videos, and polls. GEN's unique news and technology focus covers the entire bioproduct life cycle, including drug discovery, early-stage R&D, applied research (e.g., omics, biomarkers, and diagnostics), bioprocessing, and commercialization.
GEN's print magazine, which also can easily be accessed online, includes feature articles on emerging technologies, product roundups, in-depth overviews from key scientific and bioindustry meetings, and industry-standard tutorials and technical articles on drug discovery, bioprocessing, and assay technologies.

The Road to Open Science Podcast is your guide on everything Open at Utrecht University and beyond. In our monthly podcast we discuss the latest developments in the fields of open acces, open data/software, public engagement and recognition and rewards.

Serving mugs of Reproducibili☕️: Blends include transparency, openness and robustness + a spoonful of science. Brewed by the @ReproducibiliT Team

A podcast about Open Science, Open Access, Open Education, Open Data, Open Software ... pretty much «open anything». Produced by the University Library at UIT The Arctic University of Norway. Founder and host of episodes 1-31: Erik Lieungh. Host from episode 32 onwards: Per Pippin Aspaas.

New tracks tagged #reproducibility