ArticleScientific data2025
Mechanical performance dataset for alloy with applications at low temperatures.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- Mechanical performance dataset for alloy with applications at low temperatures.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Modern technologies such as liquid fuels (hydrogen, oxygen), superconductivity, and quantum technology require materials to serve at very low temperatures, pushing the bounds of material performance by demanding a combination of strength and toughness to tackle various challenges. Steel alloys are among the most commonly used materials in cryogenic applications. Meanwhile, aluminum and titanium alloys are increasingly recognized for their potential in aerospace and the transportation sectors. Emerging multi-principal element alloys such as medium-entropy and high-entropy alloys offer superior low-temperature mechanical performance, greatly expanding the space for material design. A comprehensive dataset of these low-temperature metallic alloys has been curated from literature and made available in an open repository to meet the need for validated data sources in research and development. The dataset construction workflow incorporates automated extraction using state-of-the-art machine learning and natural language processing techniques, supplemented by manual inspection and correction to improve data extraction efficiency and ensure dataset quality. The product dataset encompasses key performance parameters such as yield strength, tensile strength, elongation at fracture, and Charpy impact energy. The accompanying metadata, detailing material types, chemical compositions, processing and testing conditions, are provided in a standardized format to promote data-driven research in material screening, design, and discovery.
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.