Automated Author ProfileBonola, Marco
Bonola, Marco
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
User Mobility Characterization</purpose><purpose>Network Performance Analysis</purpose><purpose>Routing Protocol for DTNs (Disruption Tolerant Networks)</purpose><purpose>Positioning Systems</purpose><purpose>Human Behavior Modeling</purpose><purpose>Localization</purpose><purpose>Opportunistic ConnectivityDataset of mobility traces of taxi cabs in Rome, Italy.Dataset of mobility traces of taxi cabs in Rome, Italy.This dataset contains mobility traces of taxi cabs in Rome, Italy. It contains GPS coordinates of approximately 320 taxis collected over 30 days. Related publication: Marco Bonola, Lorenzo Bracciale, Pierpaolo Loreti, Raul Amici, Antonello Rabuffi, and Giuseppe Bianchi. 'Opportunistic communication in smart city: Experimental insight with small-scale taxi fleets as data carriers'. Ad Hoc Networks, February 2016.date/time of measurement start: 2014-02-01date/time of measurement end: 2014-03-02collection environment: 320 taxi drivers that work in the center of Rome. Traces present the positions of drivers, collected every 7 seconds.sanitization: Driver names have been replaced with an id.limitation: To retrieve the position, we used the getAccuracy function of Android LocationManager objects. We filter out the positions if their precision is less than 20m.Tracesetroma/taxi/taxicabsDataset of mobility traces of taxi cabs in Rome, Italy.file: taxi_february.tar.gzdescription: Dataset of mobility traces of taxi cabs in Rome, Italy.measurement purpose: User Mobility Characterization, Network Performance Analysis, Routing Protocol for DTNs (Disruption Tolerant Networks), Positioning Systems, Human Behavior Modeling, Opportunistic Connectivityroma/taxi/taxicabs Tracetaxi-rome1: 392 Mb of compressed data regarding the position of taxi cabs working for a month in the center of Rome, Italy.format: The trace is a txt file formatted as: DRIVER_ID;TIMESTAMP;POSITION Where: - Driver_id is an integer. - Timestamp includes date and time. - Position is formatted as POINT(latitude, longitude) The trace is sorted on the timestamp.
Authors
- Bracciale, Lorenzo ;
- Bonola, Marco ;
- Loreti, Pierpaolo ;
- Bianchi, Giuseppe ;
- Amici, Raul ;
- Rabuffi, Antonello