Evidence map›Paper›PMID 41927556›Full record

ArticleScientific data2026

A cyclist-centric 360

Han Li, Liangfeng Chen, Zheng Wang, Jinyu Ma, Ruiqi Xu, Kun Xia

Abstract read
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

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. A cyclist-centric 360Scientific data · 2026
    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

6 authors.

Han LiUniversity of Shanghai for Science and Technology, Shanghai, 200093, China.
Liangfeng ChenUniversity of Shanghai for Science and Technology, Shanghai, 200093, China.
Zheng WangUniversity of Shanghai for Science and Technology, Shanghai, 200093, China.
Jinyu MaUniversity of Shanghai for Science and Technology, Shanghai, 200093, China.
Ruiqi XuUniversity of Shanghai for Science and Technology, Shanghai, 200093, China.
Kun XiaUniversity of Shanghai for Science and Technology, Shanghai, 200093, China. xiakun@usst.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cycling safety is becoming an increasingly significant challenge for urban transportation, and vision-based object detection technology for cycling scenes has been widely studied. Datasets are an essential component of studying object detection in cycling scenes. However, existing datasets often have limitations, including limited scene diversity and incomplete coverage of safety-critical objects. What is particularly critical is that the data acquisition perspective is not from the perspective of real cyclists, and the limited field of view cannot cover blind spot risks. To address these limitations, we introduce PanoCycle360, a novel 360

Identifiers

PMID41927556
PMCPMC13216517

What OpenQuestion holds

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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.