Evidence map›Paper›PMID 41888532›Full record

ArticleScientific data2026

FePTP: A text-mined dataset of transformation pathways among iron-containing phases.

Le Lin, Changhai Ren, Yang Xiao, Jingyu Nie, Chongchong Qi, Xiaoqin Li, Han Wang, Zhang Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Le LinSchool of Environment and Energy, Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou, 510006, China.
Changhai RenSchool of Metallurgy and Environment, Central South University, Changsha, 410083, China.
Yang XiaoSchool of Metallurgy and Environment, Central South University, Changsha, 410083, China.
Jingyu NieSchool of Metallurgy and Environment, Central South University, Changsha, 410083, China.
Chongchong QiSchool of Metallurgy and Environment, Central South University, Changsha, 410083, China.
Xiaoqin LiSchool of Environment and Energy, Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou, 510006, China.
Han WangSchool of Metallurgy and Environment, Central South University, Changsha, 410083, China. han.wang@csu.edu.cn.
Zhang LinSchool of Metallurgy and Environment, Central South University, Changsha, 410083, China.ORCID http://orcid.org/0000-0002-6600-2055

Funding

National Natural Science Foundation of China (National Science Foundation of China) 22306204National Natural Science Foundation of China (National Science Foundation of China) 22336006National Natural Science Foundation of China (National Science Foundation of China) 22494680National Natural Science Foundation of China (National Science Foundation of China) 22494681
6 · The paper itself

Abstract

Iron, the most abundant element on Earth by mass (34.6%), primarily exists as iron minerals due to its inherent reactivity. The study of iron mineral phase transformations under changing environmental conditions remains an important research focus due to its geological, environmental, and industrial significance. Yet, the complexity of the system prevents the development of a universal principle to interpret phase transformation behaviors across diverse environmental conditions. An alternative approach is to employ data-driven methods to obtain approximate predictive results. Nevertheless, the data concerning iron-containing phase transformations remain fragmented due to a lack of standardized integration, hindering the advancement of related research. To address this gap, we have developed an automated pipeline that extracts and curates iron-containing phase transformation pathways, creating the first text-mined dataset of 11,241 pathways. Each record includes the precursor/product phases, reaction category, procedures, and associated parameters, as well as the extent of transformation and reaction equations, providing a comprehensive foundation for advancing data-driven research.

Identifiers

PMID41888532
PMCPMC13184344

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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.