Automated Organization ProfileImam Muhammad ibn Saud Islamic University
Imam Muhammad ibn Saud Islamic University
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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: 2.9 (sum of 9 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The dataset includes the CDR of Riyadh city, the Traffic Analysis Zones (TAZs) locations, proposed metro stations, demographic data about the city, and the road network. Those will be explored in the following.CDRCDR data represents a digital record containing information about a telephone call or communication session. A CDR typically includes information such as the caller's and recipient's phone numbers, the date and time of the call, the duration of the call, and any additional services or features used during the call (e.g., call forwarding, call waiting). In addition, CDRs may contain information about the type of call (voice, video, data), the caller's or recipient's location, and details about any supplementary services utilized during the call.The CDR file used is provided by STC for Riyadh and collected from 1,712 towers over a month, separated by hours. Each hour contains call details during that hour. These details include information about where calls originated and ended and at what hour of the day. TAZTraffic Analysis Zones (TAZs) are geographical areas defined and used for transportation planning and traffic analysis purposes. TAZs are created by dividing a large region or area into smaller sub-areas based on population density, land use patterns, transportation infrastructure, and socio-economic characteristics.TAZs are defined to facilitate transportation planning and analysis by providing a more granular and manageable unit for studying travel patterns and forecasting transportation demand. TAZs are often used in transportation models and simulations to estimate and analyze traffic flows, travel behavior, and travel demand within specific areas. As part of this study, Riyadh city is defined as 1,492 TAZs.Metro StationsIn this study, we consider the 84 metro stations. Based on the spatial information of each TAZ, we associated each TAZ with the closest metro station. This made it easier to predict metro usage.Demographic DataThe demographic data is available in TAZ. Each TAZ includes data on the total population of males, females, and non-Saudis. Females and non-Saudis are expected to utilize the metro more based on the sociocultural implications of the region. Hence, areas with higher concentrations of those populations expect more metro usage.Road NetworkThe road network comprises several thousand lines, each represented by numerous points defined by latitude and longitude. These points constitute nodes. These road lines are the pathways that users travel on while making a call.
Authors
- Wazrah, Asma Al ;
- AlHumoud, Sarah
Supplementary Material 1
Authors
- Hamza, Hammadi ;
- Villa, Sara ;
- Torre, Sara ;
- Marchesini, Alexis ;
- Benabderrahim, Mohamed Ali ;
- Rejili, Mokhtar ;
- Sebastiani, Federico
Additional file 2: Kronagram for Kraken2 output at confidence threshold 0.1 for Pentidionis agamae pool R9P
Authors
- Alkathiry, Hadil A. ;
- Alghamdi, Samia Q. ;
- Sinha, Amit ;
- Margos, Gabriele ;
- Stekolnikov, Alexandr A. ;
- Alagaili, Abdulaziz N. ;
- Darby, Alistair C. ;
- Makepeace, Benjamin L. ;
- Khoo, Jing Jing
Additional file 2: Kronagram for Kraken2 output at confidence threshold 0.1 for Pentidionis agamae pool R9P
Authors
- Alkathiry, Hadil A. ;
- Alghamdi, Samia Q. ;
- Sinha, Amit ;
- Margos, Gabriele ;
- Stekolnikov, Alexandr A. ;
- Alagaili, Abdulaziz N. ;
- Darby, Alistair C. ;
- Makepeace, Benjamin L. ;
- Khoo, Jing Jing
Supplementary Material 1
Authors
- Hamza, Hammadi ;
- Villa, Sara ;
- Torre, Sara ;
- Marchesini, Alexis ;
- Benabderrahim, Mohamed Ali ;
- Rejili, Mokhtar ;
- Sebastiani, Federico
Additional file 3: Kronagram for Kraken2 output at confidence threshold 0.1 for Pentidionis agamae pool Pa1
Authors
- Alkathiry, Hadil A. ;
- Alghamdi, Samia Q. ;
- Sinha, Amit ;
- Margos, Gabriele ;
- Stekolnikov, Alexandr A. ;
- Alagaili, Abdulaziz N. ;
- Darby, Alistair C. ;
- Makepeace, Benjamin L. ;
- Khoo, Jing Jing
Additional file 4: Kronagram for Kraken2 output at confidence threshold 0.1 for Pentidionis agamae pool Pa2
Authors
- Alkathiry, Hadil A. ;
- Alghamdi, Samia Q. ;
- Sinha, Amit ;
- Margos, Gabriele ;
- Stekolnikov, Alexandr A. ;
- Alagaili, Abdulaziz N. ;
- Darby, Alistair C. ;
- Makepeace, Benjamin L. ;
- Khoo, Jing Jing
Additional file 4: Kronagram for Kraken2 output at confidence threshold 0.1 for Pentidionis agamae pool Pa2
Authors
- Alkathiry, Hadil A. ;
- Alghamdi, Samia Q. ;
- Sinha, Amit ;
- Margos, Gabriele ;
- Stekolnikov, Alexandr A. ;
- Alagaili, Abdulaziz N. ;
- Darby, Alistair C. ;
- Makepeace, Benjamin L. ;
- Khoo, Jing Jing
Additional file 3: Kronagram for Kraken2 output at confidence threshold 0.1 for Pentidionis agamae pool Pa1
Authors
- Alkathiry, Hadil A. ;
- Alghamdi, Samia Q. ;
- Sinha, Amit ;
- Margos, Gabriele ;
- Stekolnikov, Alexandr A. ;
- Alagaili, Abdulaziz N. ;
- Darby, Alistair C. ;
- Makepeace, Benjamin L. ;
- Khoo, Jing Jing