Nice work! Can we skip a year for great new books at some point please, my TBR pile will be the end of me already

6/6: Next Steps
Ultimately, checklists &manual review won't scale. We need a new class of community-developed tools to vet datasets for statistical anomalies and physically implausible results before they are published or used to train future generations of AI models.

#AI #DataScience #OpenSource

5/6: The Real Fix: Mandate Raw Data
The single most important step? Journals must require raw data files (.raw, .xrdml etc), not just processed plots. Raw data is harder to fake (though not impossible). This one change could be transformative.

#FAIRdata #OpenData

4/6: A Solution: Checklists
We propose a practical checklist for reviewers and authors alike. It provides clear, technique-specific validation steps to make it much harder for poor or fraudulent data to get published.

#PeerReview #OpenScience

3/6: Example 2: Batteries
My field is a key target. GenAI could easily "smooth" noisy battery cycling data, hide capacity fade, or invent a few extra cycles to make a material look more stable than it is. It's a potential nightmare.

#Batteries #Electrochemistry

2/6: It's Deeper Than Just Images
XRD isn't safe. Published Rietveld refinements are often rife with errors: impossible stats (χ² < 1.0), missing R-factors, and physically absurd parameters like negative ADPs.

#Crystallography #Diffraction

1/6: The "Garbage In, Garbage Out" Problem
AI models learn from the published literature. But studies show our data is already riddled with errors - like a review finding over 40% of XPS papers had significant errors. AI will just learn and amplify these flaws.

A short thread on the specifics, because this is a challenge for the here and now. 🧵

I think it's a problem that needs serious attention. AI will revolutionise materials science, but also makes it dangerously easy to fabricate or manipulate data. The traditional peer review process is not equipped to handle this threat.

#MaterialsScience #AI #AcademicIntegrity #PublishOrPerish

From aerospace engineering studies to restoring Italian grinding machines and bringing vintage scissor patterns back to life - this is exactly the kind of hands-on, real-world problem solving we love to see from our students and researchers.
Sheffield Made. Sheffield Innovation. 🔴⚪

This project beautifully demonstrates how our facilities and talent support local industry. The metal prototypes from the Royce Centre allowed Ernest Wright to iterate their designs efficiently while maintaining their traditional hot drop forging process.

What makes this extra special: Elliott did his UG degree here; the Royce Centre's 3D printing technician George did his PhD with us (In-Situ Monitoring & Control during Laser Powder Bed Fusion of Nickel Superalloys) & continues to work in our School of Chemical, Materials and Biological Engineering.

Elliott Nurcombe helped Ernest Wright & Co redesign their vintage Nurseryman Scissors using metal 3D printing from our Royce Centre.

It must be soul-destroying for those who don’t.

Hope it gets sorted.

It’s a disgrace and the form is unfit for purpose. I seem to remember we wrote ‘see attached statement’ in most of the text boxes and then attached a lengthy statement that states the case in full in the way the case needed to be told.

It’s just a nightmare. Worth the fuss if you get support but…

This is a really great idea

Apparently there’s a course I can do…

Decide to give it another go. First word: FEET.

Not in the list again. Sigh