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27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “IoT data”
CoNEXT21: Mind the Gap: Multi-hop IPv6 over BLE in the IoT - Experiment Result Data
<p>This dataset contains the all raw experiment data that was used in our paper "Mind the Gap: Multi-hop IPv6 over BLE in the IoT" published at CoNEXT21.</p>
An Exploratory Study on Code Quality, Testing, Data Accuracy, and Practical Use Cases of IoT Wearables
<p>Data used in IoT Wearable Study</p>
QoS data set for IoT services
<p>This is a dataset on Quality of Service (QoS) for IoT services related to experiments. It consists of data on four QoS attributes for services generated within specified ranges, denoted as Execution time, Service cost, Credibility, and Reliability. The data has been normalized.There are some different IoTS scales in this dataset, including 6×25,6×50,6×75,6×100;10×25,10×50,10×75,10×100;20×25,20×50,20×75,20×100.<br> </p>
UNSW IoT traffic data with packets, flows, and protocols
Open the record for dataset details and reuse information.
Data of Building information modelling (BIM), Historic BIM (HBIM), Digital Twins and IoT
Open the record for dataset details and reuse information.
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>
CSV data from GPS livestock collars / Datos CSV de coordenadas GPS de dispositivos IoT sobre animales
<p>Datos recopilados de los dispositivos GPS instalados en vacas en un usuario del proyecto AIREGAN financiado por Red.es Gobierno de España</p><p>Data collected from GPS devices installed on cows in a user of the AIREGAN project, funded by Red.es (Spanish Government)</p>
ScienceDex guides
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.