Review Details
Reviewer has chosen to be Anonymous
Overall Impression: Bad
Suggested Decision: Reject
Technical Quality of the paper: Weak
Presentation: Weak
Reviewer`s confidence: High
Significance: High significance
Background: Incomplete or inappropriate
Novelty: Lack of novelty
Data availability: All used and produced data (if any) are FAIR and openly available in established data repositories
Length of the manuscript: The authors need to elaborate more on certain aspects and the manuscript should therefore be extended (if the general length limit is already reached, I urge the editor to allow for an exception)
Summary of paper in a few sentences:
This paper presents a dataset collected from a user of electric vehicle in France over a period of two years. It briefly describes different variables, data collection process, and provides few visualisations. The data was collected using an android application CanZE which collects data from a bluetooth dongle planted in the car. The data collection is therefore only possible when the user is in the proximity of the vehicle.
Reasons to accept:
The paper addresses a societally important topic in the energy transition.
Reasons to reject:
The introduction lacks specific motivation and justification as to why this data could be useful to other researchers. Authors provide a general motivation as to why electrical vehicles are important and what problems remain open concerning them such as proper infrastructure planning. However, what is lacking is the motivation and importance of this specific dataset. Why is this specific data useful? What downstream application would benefit from it? Which specific variables could be leveraged to support goals described in introduction? Why collection on personal level is important? What does it provide? Why is it necessary? Moreover how is data from only one user useful? The authors claim that it is a starting point to obtain feedback before data from other participants is collected and processed but it is even unclear how the data from one user could be leveraged to come up with such a feedback. The introduction would need a lot of work to better justify the novelty, and usefulness of this resource. "As the study that produced this dataset potentially scales, it promises to transform into a wellspring of information, nurturing the growth of novel applications and strategies for mobility research." - what are these applications and strategies? Authors should be more specific about their motivation.
Proper background information, related work is also minimal. Authors mention two general datasets about electric vehicles but do not properly position themselves in this line of work. How is this dataset different from others, why are the others not enough, what kind of a gap are authors trying to fill in with the work presented?
It looks like this paper was written in a hurry, there are multiple typos, the diagram in Figure 1 is not even fully translated to English and the style leaves a lot of room for improvement (e.g repetitions). Perhaps the paper was translated from French since the word 'climatization' is used but it seems like the authors mean 'air-conditioning'.
Authors mention python code used to process the data, I don't see it being available for review.
The structure and terminology is not strict, authors do not stick to their own terminology, e.g 'timescale' and 'timeseries' seem to be used interchangeably without any explanation. The word 'dataset' is also used to refer to the whole dataset but also to different files that were collected.
Quality control methods section - this section only introduces confusion, what is meant by frequency of the variables? Do authors mean number of unique values? It is unclear how many variables are available in the dataset, 13X, 139 or 5015? The documentation is poor and not detailed enough.
Authors mention twice that "obvious outliers were removed" - however they do not specify what is an obvious outlier and how it was detected, why those emerge or any deliberation on why it was useful to remove them instead of leaving them for the researchers working with the dataset to decide.
Figure 7 is a pie-chart. It is widely known that pie-charts are not good visualisation tool as humans are not good at judging size of an area compared to lengths.
https://scc.ms.unimelb.edu.au/resources/data-visualisation-and-explorati...
Font in figure 7 and 8 is too small, and titles are both on the figure and in the manuscript.
Limitations section does not mention the obvious limitation which is that the data is only from one participant. Authors mention that the lack of geographic data is a limitation of this dataset but in the introduction they argue that electric vehicle datasets are important for 'facilitating the optimal positioning of charging stations across urban and rural landscapes.` It's a great motivation but sine this data is missing it even further jeopardises the usefulness of this dataset.
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1 Comment
meta-review by editor
Submitted by Tobias Kuhn on
The paper addresses an interesting use case of collection of EV data although it does not go far enough. Collecting data from one user significantly limits the paper both in understanding if the technology can scale as well as the possibilities of dataset use. The work should be expanded to truly make a significant contribution.
The authors should also pay more attention to grammatical and lexical cohesion.
Christine Chichester (https://orcid.org/0000-0001-6818-334X)