ArticleScientific data2022
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Review
- Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.Nature methods · 2026Review
- From FAIR to CURE: guidelines for computational models of biological systems.NPJ systems biology and applications · 2026Review
- AI for scientific discovery is a social problem.Patterns (New York, N.Y.) · 2026Review
- Autonomous artificial intelligence, scientific research, and human values.AI and ethics · 2026Article
- Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense-response-heal platforms.Materials today. Bio · 2025Review
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- A Cloud-Based System for Automated AI Image Analysis and Reporting.Journal of imaging informatics in medicine · 2025Article
- Privacy, ethics, transparency, and accountability in AI systems for wearable devices.Frontiers in digital health · 2025Review
- CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.JAMIA open · 2024Article
- How I read an article that uses machine learning methods.Blood advances · 2023Article
- FAIR for AI: An interdisciplinary and international community building perspective.Scientific data · 2023Article
- Linking scientific instruments and computation: Patterns, technologies, and experiences.Patterns (New York, N.Y.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.