ArticleScientific data2025
A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm.
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 1 paper.
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
1 citing paper in PubMed.
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
An auto-generated dataset of Curie and Néel temperatures is presented, containing 56,037 records extracted from 108,181 published scientific articles. Each record contains the extracted chemical entity and associated extracted temperature. The dataset was auto-generated by mining text from the papers using the 'chemistry-aware' natural-language-processing toolkit, ChemDataExtractor 2.2.2, in conjunction with the Snowball v2 parser, which has been adapted to extract Curie and Néel temperatures from scientific text. This dataset is the first of its kind to be generated using the Snowball v2 parser, with its evaluation yielding a precision of 72% and a recall of 61%. The public availability of this dataset will aid in the design, prediction and analysis of magnetic materials.
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.