Evidence map›Paper›PMID 36309509›Full record

ArticleScientific data2022

Multiorder hydrologic Position for Europe - a Set of Features for Machine Learning and Analysis in Hydrology.

Maximilian Nölscher, Michael Mutz, Stefan Broda

Abstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Maximilian NölscherFederal Institute for Geosciences and Natural Resources (BGR), Berlin, 13593, Germany. max-n@posteo.de.ORCID 0000-0001-5606-1900
Michael MutzIndependent researcher, Berlin, Germany.
Stefan BrodaFederal Institute for Geosciences and Natural Resources (BGR), Berlin, 13593, Germany.ORCID 0000-0001-6858-6368

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The presented dataset EU-MOHP v013.1.1 provides multiscale information on the hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures "divide to stream distance" (DSD) as sum of the distances to the nearest stream and catchment divide, "lateral position" (LP) as a relative measure of the position between the nearest stream and divide and "stream distance" (SD) as the distance to the nearest stream. These three measures are calculated for nine hydrologic orders to reflect different spatial scales from local to continental. Its spatial extent covers major parts of the European Economic Area (EEA39) which also largely coincides with physiographical Europe. Although there are multiple potential use cases, this dataset serves predominantly as valuable static environmental descriptor or predictor variable for hydrogeological and hydrological modelling such as mapping or forecasting tasks using machine learning. The generation of this dataset uses free open source software only and therefore can be transferred to other regions or input datasets.

Identifiers

PMID36309509
PMCPMC9617849

What OpenQuestion holds

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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.