Evidence map›Paper›PMID 40419587›Full record

ArticleScientific data2025

A global scale submarine landform dataset driven by terrain knowledge.

Fengyize Yu, Liyang Xiong, Hongen Wang, Guoan Tang, Josef Strobl

Abstract read
In one paragraph

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.

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

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.

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

5 authors.

Fengyize YuSchool of Geography, Nanjing Normal University, Nanjing, 210023, China.
Liyang XiongSchool of Geography, Nanjing Normal University, Nanjing, 210023, China. xiongliyang@njnu.edu.cn.ORCID http://orcid.org/0000-0001-7930-3319
Hongen WangSchool of Geography, Nanjing Normal University, Nanjing, 210023, China.
Guoan TangSchool of Geography, Nanjing Normal University, Nanjing, 210023, China.
Josef StroblKey Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China.ORCID http://orcid.org/0000-0001-6234-9812

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42371407
6 · The paper itself

Abstract

Submarine landforms are a critical component of Earth's geomorphology, essential for understanding marine geological evolution, ocean dynamics, and marine ecosystems. However, global-scale classification of submarine landforms has been constrained by the lack of high-resolution data and insufficient integration of terrain knowledge. In response to these challenges, we propose a terrain knowledge-based submarine landform framework, which considers morphological features, spatial relations and undulating characteristics. Utilizing the General Bathymetric Chart of the Oceans at 15 arc second, a dataset of global submarine landform (GSL) is produced. The dataset includes the submarine landforms with 6 landform zones and 21 landform types, which reveals the diversity and complexity of submarine landforms. The comparison with existing 30 arc second global seafloor feature maps reveals that our dataset can reflect more detailed regional characteristics of the seafloor geomorphology. This dataset is the first global scale submarine landform dataset at 15 arc second, which offers a new perspective on submarine landforms, providing key insights into seafloor geology, morphology, and dynamic processes.

Identifiers

PMID40419587
PMCPMC12106648

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Registered trials

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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.