Contributors:Thomas Dean, Hands Simon, Borgo Rita, Laramee Robert
The provided files contain the data used in this case study.
"config.b190k1680mu0700j02s16t08" contains the raw configuration data as (binary) output from the FORTRAN 'cooling' code.
"topological charge density.hvol" captures the scalar field in 4-dimensions by computing the topological charge density at each site on the lattice.
"cool0030_sliced.7z" contains each 3D slice of the data across the temporal axis.
Read me files are provided for parsing the scalar field in 4D ("data_structure.txt") and 3D ("sliced_data_structure.txt").
It is vital to capture and analyze, from various sources in smart cities, the data that are beneficial in urban planning and decision making for governments and individuals. Urban policy makers can find a suitable solution for urban development by using the opportunities and capacities of big data, and by combining different heterogeneous data resources in smart cities. This paper presents data related to urban computing with an aim of assessing the knowledge that can be obtained through integration of multiple independent data sources in Smart Cities. The data contains multiple sources in the city of Aarhus, Denmark from August 1, 2014 to September 30, 2014. The sources include land use, waterways, water barriers, buildings, roads, amenities, POI, weather, traffic, pollution, and parking lot data. The published data in this paper is an extended version of the City Pulse project data to which additional data sources collected from online sources have been added.
The data includes runtime information on the re-computation of the SVI process. This includes re-computation following changes in ClinVar and GeneMap databases in different scenarios presented in the paper: blind re-computation, partial re-computation, partial re-computation with input difference and scoped partial re-computation with input difference. Interested reader please contact authors for more detailed explanation.
182 simulated datasets (first set contains small datasets and second set contains large datasets) with different cluster compositions – i.e., different number clusters and separation values – generated using clusterGeneration package in R. Each set of simulation datasets consists of 91 datasets in comma separated values (csv) format (total of 182 csv files) with 3-15 clusters and 0.1 to 0.7 separation values. Separation values can range between (−0.999, 0.999), where a higher separation value indicates cluster structure with more separable clusters.
Size of the dataset, number of clusters, and separation value of the clusters in the dataset is printed in file name. size_X_n_Y_sepval_Z.csv:
Size of the dataset = X
number of clusters in the dataset = Y
separation value of the clusters in the dataset = Z