The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A multi-source dataset of urban life in the city of Milan and the Province of Trentino
Open accessAbstract
The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conference · doi:10.25080/majora-92bf1922-00a · Cited by 11,032
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,059
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Quantifying the Impact of Human Mobility on Malaria
Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., & Buckee, C. O. (2012) · Science · doi:10.1126/science.1223467 · Cited by 992
Dynamic population mapping using mobile phone data
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., Blondel, V. D., & Tatem, A. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408439111 · Cited by 900
Network Diversity and Economic Development
Eagle, N., Macy, M., & Claxton, R. (2010) · Science · doi:10.1126/science.1186605 · Cited by 770
Geo-located Twitter as proxy for global mobility patterns
Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014) · Cartography and Geographic Information Science · doi:10.1080/15230406.2014.890072 · Cited by 673
A survey of results on mobile phone datasets analysis
Blondel, V. D., Decuyper, A., & Krings, G. (2015) · EPJ Data Science · doi:10.1140/epjds/s13688-015-0046-0 · Cited by 647
From mobile phone data to the spatial structure of cities
Louail, T., Lenormand, M., Cantu Ros, O. G., Picornell, M., Herranz, R., Frias-Martinez, E., Ramasco, J. J., & Barthelemy, M. (2014) · Scientific Reports · doi:10.1038/srep05276 · Cited by 456
Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data
Zhang, C., Zhang, H., Qiao, J., Yuan, D., & Zhang, M. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904363 · Cited by 373
Survey on traffic prediction in smart cities
Nagy, A. M., & Simon, V. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.07.004 · Cited by 312
Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02264-y · Cited by 276
Pappalardo, L., Simini, F., Barlacchi, G., & Pellungrini, R. (2022) · Journal of Statistical Software · doi:10.18637/jss.v103.i04 · Cited by 102
Self-citationSplit Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions
Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3351600 · Cited by 90
Network Slicing-Based Learning Techniques for IoV in 5G and Beyond Networks
Hamdi, W., Ksouri, C., Bulut, H., & Mosbah, M. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3372083 · Cited by 63
Trinh, H. D., Fernandez Gambin, A., Giupponi, L., Rossi, M., & Dini, P. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3028197 · Cited by 40
Metadata
- Authors
- Barlacchi, G., De Nadai, M., Larcher, R., Casella, A., Chitic, C., Torrisi, G., Antonelli, F., Vespignani, A., Pentland, A., & Lepri, B.
- Venue
- Scientific Data
- Published
- 2015-10-27
- Volume
- 2
- Issue
- 1
- Pages
- 150055-150055
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2190432600
- Cited by
- 560
- References
- 33
- Type
- data paper
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
