Evidence map›Paper›PMID 37456122›Full record

ArticleData in brief2023

A 5 m dataset of digital terrain model derivatives across mainland France.

Léa Panhelleux, Sébastien Rapinel, Blandine Lemercier, Guillaume Gayet, Laurence Hubert-Moy

Abstract read
In one paragraph

Article in Data in brief, 2023. 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.

Léa PanhelleuxLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri Le Moal, Rennes, 35000, France.
Sébastien RapinelLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri Le Moal, Rennes, 35000, France.
Blandine LemercierSAS UMR 1069, Institut Agro Rennes-Angers - INRAE, 65 rue de Saint-Brieuc, Rennes, 35000, France.
Guillaume GayetPatriNat OFB-MNHN-CNRS-IRD, 57 rue Cuvier, Paris, 75231, France.
Laurence Hubert-MoyLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri Le Moal, Rennes, 35000, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A dataset of three digital terrain model (DTM) derivatives was produced at 5 m spatial resolution across mainland France. This dataset includes (i) a topographic wetness index (TWI) that characterizes potential soil wetness as a function of the contributing area and local slope, (ii) a multi-scale topographic position color composite (MTPCC) that describes the position of a pixel relative to its neighborhood at three spatial scales, and (iii) a vertical distance to channel network index (VDCNI) that expresses the vertical height between the elevation of a pixel and the nearest channel. These three raster layers were derived from the French national airborne DTM at 5 m spatial resolution and the vector layer of the channel network of the national hydrological database. This unprecedented fine-scale dataset opens new insights for geomorphological analysis. It can be used for several purposes, such as environmental modeling, risk assessment, or water-resource management.

Indexed as

Big dataDTMEnvironmental modelingHydrogeomorphologySoil moistureWetland

Identifiers

PMID37456122
PMCPMC10339187

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.