SwissRailNet: Four representations of a spatially embedded railway network dataset.

Martin Sterchi, Emily Raubach, Lorenz Hilfiker

Abstract read

In one paragraph

Article in Data in brief, 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.

SwissRailNet: Four representations of a spatially embedded railway network dataset.Data in brief · 2025
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

3 authors.

Martin SterchiSchool of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Riggenbachstrasse 16, 4600 Olten, Switzerland.

Emily RaubachInstitute of Psychology, University of Bern, Fabrikstrasse 8, 3012 Bern, Switzerland.

Lorenz HilfikerSchool of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Riggenbachstrasse 16, 4600 Olten, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public transport networks are frequently studied in the field of network science, including applications in epidemic modeling, infrastructure optimization, and machine learning. This paper introduces four representations of a Swiss railway network. The dataset is derived from open data sources of Swiss public transport companies and is the result of several data processing steps as well as a manual validation. In all four representations, nodes represent train stations, embedded in geographic space, making the network an example of a spatial network influenced by physical constraints such as mountains and lakes. The edge definitions vary, however. Edges connect (1) any pair of nodes with a train connection for which no train change is required, (2) any pair of nodes with a non-stop train connection, and (3) any pair of nodes that correspond to physically adjacent stations. The fourth representation corresponds to a temporal network in which each edge is attributed with the start and duration of a train connection.

Indexed as

Public transportSpatial networksStatic networksTemporal networks

Identifiers

PMID41362350

DOI10.1016/j.dib.2025.112266

PMCPMC12681929

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.