ArticleScientific data2026
Comprehensive spatiotemporal dataset of shared bicycle operations in Xiamen China.
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 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Comprehensive spatiotemporal dataset of shared bicycle operations in Xiamen China.Scientific data · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We present a comprehensive spatiotemporal dataset of shared bicycle operations in Xiamen, China, collected over five consecutive weekdays (December 21-25, 2020) during morning peak hours (6:00-10:00 AM). The dataset comprises three components: (1) 220,675 origin-destination trip records from 53,630 unique bicycles, including precise GPS coordinates, timestamps, and lock status; (2) 2,849,243 GPS trajectory tracking points captured at 30-second to 2-minute intervals during complete trips; (3) 14,071 electronic fence records with unique identifiers and four-corner coordinates specifying parking zone boundaries. The dataset captures environmental variations including weather impacts and maintains strict privacy protection through cryptographic anonymization. This open dataset enables analysis of urban mobility patterns, cycling behavior, and demand forecasting. We demonstrate the dataset's utility through temporal distribution analysis, spatial pattern visualization, regional clustering, and demand prediction using neural networks. The dataset supports research in urban transportation planning, shared mobility optimization, and smart city applications.
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Registered trials
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.