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68 results for “Flow networks”
Data from: Familiarity affects network structure and information flow in guppy (Poecilia reticulata) shoals
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Data from: The response of migratory populations to phenological change: a Migratory Flow Network modelling approach
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IoT-deNAT: Outbound flow-based network traffic data of IoT and non-IoT devices behind a home NAT
<p>This dataset is comprised of NetFlow records, which capture the outbound network traffic of 8 commercial IoT devices and 5 non-IoT devices, collected during a period of 37 days in a lab at Ben-Gurion University of The Negev. The dataset was collected in order to develop a method for telecommunication providers to detect vulnerable IoT models behind home NATs. Each NetFlow record is labeled with the device model which produced it; for research reproducibilty, each NetFlow is also allocated to either the "training" or "test" set, in accordance with the partitioning described in:</p> <p>Y. Meidan, V. Sachidananda, H. Peng, R. Sagron, Y. Elovici, and A. Shabtai, A novel approach for detecting vulnerable IoT devices connected behind a home NAT, Computers & Security, Volume 97, 2020, 101968, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2020.101968. (http://www.sciencedirect.com/science/article/pii/S0167404820302418)</p> <p> </p> <p>Please note:</p> <ul> <li>The dataset itself is free to use, however users are requested to cite the above-mentioned paper, which describes in detail the research objectives as well as the data collection, preparation and analysis.</li> <li>Following is a brief description of the features used in this dataset.</li> </ul> <p> </p> <p># NetFlow features, used in the related paper for analysis</p> <p>'FIRST_SWITCHED': System uptime at which the first packet of this flow was switched<br> 'IN_BYTES': Incoming counter for the number of bytes associated with an IP Flow<br> 'IN_PKTS': Incoming counter for the number of packets associated with an IP Flow<br> 'IPV4_DST_ADDR': IPv4 destination address<br> 'L4_DST_PORT': TCP/UDP destination port number<br> 'L4_SRC_PORT': TCP/UDP source port number<br> 'LAST_SWITCHED': System uptime at which the last packet of this flow was switched<br> 'PROTOCOL': IP protocol byte (6: TCP, 17: UDP)<br> 'SRC_TOS': Type of Service byte setting when there is an incoming interface<br> 'TCP_FLAGS': Cumulative of all the TCP flags seen for this flow</p> <p> </p> <p># Features added by the authors</p> <p>'IP': Prefix of the destination IP address, representing the network (without the host)<br> 'DURATION': Time (seconds) between first/last packet switching</p> <p> </p> <p># Label<br> 'device_model': <type>.<manufacturer>.<model number></p> <p> </p> <p># Partition<br> 'partition': Training or test</p> <p> </p> <p># Additional NetFlow features (mostly zero-variance)<br> 'SRC_AS': Source BGP autonomous system number<br> 'DST_AS': Destination BGP autonomous system number<br> 'INPUT_SNMP': Input interface index<br> 'OUTPUT_SNMP': Output interface index<br> 'IPV4_SRC_ADDR': IPv4 source address<br> 'MAC': MAC address of the source</p> <p> </p> <p># Additional data<br> 'category': IoT or non-IoT<br> 'type': IoT, access_point, smartphone, laptop<br> 'date': Datepart of FIRST_SWITCHED<br> 'inter_arrival_time': Time (seconds) between successive flows of the same device (identified by its MAC address)</p>
Migration flow and complex network
<p>Data is collected from Baidu Map (http://qianxi.baidu.com/). The migration scale index data, produced by the map service company of Baidu through the GPS location in mobile phone, record the number of migrants between any two cities. It visualises the migration flows in particular time periods. Although, it cannot capture all migrants, it is useful to conduct comparisons among different cities.</p>
Flow estimates example for k = 50 - Information-Theoretic Sensor Placement for Large-Scale Sewer Networks
<p>This dataset is used to generate Figure 7 without running the simulations in the paper Information-Theoretic Sensor Placement for Large-Scale Sewer Networks.</p>
Solving the multi-commodity flow problem using an evolutionary routing algorithm in a computer network environment
<p>The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that takes into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Networks (SDN) controller. The use of such information ensures that no link is used beyond its capacity, eliminating network congestion. We use EAs as true multi-objective solvers to provide a set of valid routing solutions from a single run of the algorithm. Modifications to the Multipath TCP (MPTCP) protocol are proposed to overcome the multipath problems associated with TCP.</p>
Fig. 5 a 95 in Using haplotype networks, estimation of gene flow and phenotypic characters to understand species delimitation in fungi of a predominantly Antarctic Usnea group (Ascomycota, Parmeliaceae)
Fig. 5 a 95% probability haplotype network for the U. sphacelata group based on a combined dataset of ribosomal IGS and ITS and protein-coding RPB1 sequences. The 3-step nesting level is shown. Haplotypes are represented by colored circles according to the sample localities. The circle size is proportional to the number of sequences sharing a haplotype, except for haplotype H5 comprising too many
Remodeling of self-assembled microvascular networks under long term flow
GEO Series GSE294363. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.