ReviewNature2022
FAIR data enabling new horizons for materials research.
Review in Nature, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 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
61 citing papers in PubMed.
- Rethinking catalysis: interpretable AI and description of real-world conditionsFaraday discussions · 2026Article
- From empirical imprinting to programmable synthetic receptors: a perspective on data science and machine learning for molecularly imprinted polymers.Polymer chemistry · 2026Review
- SmartHisto: Bayesian active learning for histology images.PLoS computational biology · 2026Article
- Soft, Reactive, and Alive: A Dynamic Framework for Degradation and Functional Stability of Polymeric Biomaterials.Polymers · 2026Review
- Digital infrastructure to support and share daily high-throughput experimental materials research.Faraday discussions · 2026Article
- Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions.Materials (Basel, Switzerland) · 2026Review
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- OpenStats: how to combine statistics and research data management (RDM) to leverage efficient scientific data analysis by guided statistics.Journal of cheminformatics · 2026Article
- Journal research data policies in materials science.Digital discovery · 2026Review
- A new AI assisted approach aligns data standards and accelerates interoperability in biomedical research.NPJ digital medicine · 2026Article
- Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.Journal of chemical information and modeling · 2026Article
- Carrier-Free Enzyme Immobilization for High-Density Catalytic Architectures in Flow Biocatalysis.JACS Au · 2026Review
- Evolve with your research: stepwise system evolution from document-driven to fact-centric research data management in materials science.Journal of cheminformatics · 2026Article
- Generative Models for Crystalline Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Review
- Acquisition, quality control, and architecture of a large image dataset as a tool for in silico cell biological research.Materials today. Bio · 2025Article
- Blockchain Technology for Big-data Sharing in Material Genome Engineering.Scientific data · 2025Article
- Accelerated data-driven materials science with the Materials Project.Nature materials · 2025Review
- The Enduring Relevance of Semiempirical Quantum Mechanics.The journal of physical chemistry. A · 2025Review
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.
Indexed as
Identifiers
35478233What 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.