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The contact tracing dataset consisted of 254 individuals (cases and contacts, of both poultry and humans) who were linked through a network of social contacts.
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During T4E, the objective was to introduce iSAT as a visual learning analytics tool to trace educational dataset.
In this paper, we analyzed the spatial and temporal dynamics of VANETs based on two real taxi-trace datasets.
This paper explores the spatial and temporal features of vehicular network topologies based on two real taxi-trace datasets.
The purpose of this paper is to reveal the deep understanding of spatial and temporal dynamics of VANETs based on two real taxi-trace datasets collected from San Francisco, USA and Shenzhen, China.
The rest of the paper is organized as follows: Section 2 briefly summarizes the related work, Section 3 describes the definitions used in spatial and temporal analysis for VANETs, and Section 4 introduces the two taxi-trace datasets and presents the spatial and temporal discoveries as well as their implications for VANET design and protocol optimization.
We applied our methylation dynamics model to an RRBS dataset tracing the differentiation of adult HSCs (Bock et al., 2012).
We perform the lookups using pseudo-random generated IP addresses and addresses from a real packet trace in the "CAIDA Anonymized Internet Traces 2016" dataset [41].
In earlier works, mainly researchers used the SAT diagram to trace transitions in dataset.
The first of these is the discontinuous nature of the location traces in our dataset.
While this community-driven process is not directly reflected by the data stored in the OSM database, information about this process can indirectly be concluded by understanding local differences between the data of different areas, by tracing the heterogeneity of the dataset, and by comparing the data with other information, for example, aerial images.
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