ArticleScientific data2025
A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis.
Article in Scientific data, 2025. 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
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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.
- An AI-Augmented Dataset of Multi-Prototype Electric Vehicle Charging Load Profiles in China.Scientific data · 2026Article
- A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With increasing policy and market support for electric vehicles (EVs) worldwide, analyzing EV charging demand is crucial for jointly optimizing transportation and energy systems. However, existing public datasets typically suffer from limited global coverage, coarse temporal resolution, and narrow feature availability. Here, we present CHARGED, a city-scale and harmonized dataset for global electric vehicle charging demand analysis. CHARGED contains hourly records from April 1 to September 30, 2023, covering about 12,000 charging chargers across six representative cities on six continents, including Amsterdam, Johannesburg, Los Angeles, Melbourne, São Paulo, and Shenzhen. Each entry encompasses core charging metrics (duration, volume, electricity price, and service price) alongside rich auxiliary information (weather variables, geospatial attributes, and multi-level static descriptors). CHARGED fills existing gaps and provides standardized data with spatiotemporal features aligned and multi-source information harmonized. Technical validation shows the potential of CHARGED to support in-depth characterization of user charging demand, and to impel the study of more advanced machine learning models, especially those enabling transfer learning across diverse urban contexts.
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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.