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77 results for “home environment”
Robot@Home2, a robotic dataset of home environments
<p>The Robot-at-Home dataset (<strong>Robot@Home</strong>, paper <a href="http://mapir.uma.es/papersrepo/2017/2017-raul-IJRR-Robot_at_home_dataset.pdf">here</a>) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.</p> <p>This dataset is unique in three aspects:</p> <ul> <li>The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.</li> <li>It comprises diverse and numerous data: <em>sequences of RGB-D images and laser scans</em> from the rooms of five apartments (87,000+ observations were collected), <em>topological information</em> about the connectivity of these rooms, and <em>3D reconstructions</em> and <em>2D geometric maps</em> of the visited rooms.</li> <li>The provided ground truth is dense, including <em>per-point annotations</em> of the categories of the objects and rooms appearing in the reconstructed scenarios, and <em>per-pixel annotations</em> of each RGB-D image within the recorded sequences</li> </ul> <p>During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like <em>pillows are usually on beds</em> or <em>ovens are not in bathrooms</em>.</p> <p><strong>Robot@Home2</strong></p> <p><a href="https://github.com/goyoambrosio/RobotAtHome2">Robot@Home2</a>, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a <a href="#bottom"><strong>relational database</strong></a> that states the contextual information and data links, compatible with Standard Query Language. Secondly,a <a href="https://pypi.org/project/robotathome/"><strong>Python package</strong></a> for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of <a href="https://drive.google.com/drive/folders/1ENnxbKP5MJdlGl2Q93WTbIlofuy6Icxq"><strong>Jupyter notebooks</strong></a>, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.</p> <p>If you use Robot@Home2, please cite the following <a href="https://www.sciencedirect.com/science/article/pii/S2352711023001863">paper</a>:</p> <p>Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, <strong>The Robot@Home2 dataset: A new release with improved usability tools</strong>, in <em>SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110</em>, <a href="https://doi.org/10.1016/j.softx.2023.101490">https://doi.org/10.1016/j.softx.2023.101490</a>.</p> <blockquote>@article{ambrosio2023robotathome2,<br>title = {The Robot@Home2 dataset: A new release with improved usability tools},<br>author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez},<br>journal = {SoftwareX},<br>volume = {23},<br>pages = {101490},<br>year = {2023},<br>issn = {2352-7110},<br>doi = {https://doi.org/10.1016/j.softx.2023.101490},<br>url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863},<br>keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab}<br>}<br> </blockquote> <p><strong>Version history</strong><br><a href="../record/3901564">v1.0.1</a> Fixed minor bugs.<br><a href="../record/4495821">v1.0.2</a> Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version.<br><a href="../record/4499043">v2.0.0</a> SQL based dataset. Robot@Home v1.0.2 has been packed into a <a href="https://www.sqlite.org/index.html">sqlite</a> database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the <a href="https://github.com/goyoambrosio/RobotAtHome2">toolbox</a>.<br><a href="../record/4530453">v2.0.1</a> A forgotten foreign key pair have been added.<br><a href="../records/7811783">v.2.0.2</a> The views have been consolidated as tables which allows a considerable improvement in access time.<br><a href="../records/7811795">v.2.0.3</a> The previous version does not include the database. In this version the database has been uploaded.<br>v.2.1.0 Depth images have been updated to 16-bit. Additionally, both the RGB images and the depth images are oriented in the original camera format, i.e. landscape.</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Home environment
<p>For the home environment we have: 01 Wifi Modem Router, 03 Smartphones, 01 server, 01 desktop, 01 Multifunction Printer, 01 network extender, 01 SmartTV, 01 Cable TV decoder and 01 firewall. This environment is a local network. The server has the Monitoring Environment and a network card, which provides connectivity and receives all network traffic for analysis.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-09-15 to 2019-02-04</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Internal environment
<p>In this environment we have: 01 Wifi Router, 01 Smartphone, 01 server and 01 desktop with virtual machines. This environment, called Internal, is a local network. One of the servers has the Security and Performance Monitoring Environment installed. In addition, 05 virtual machines were instantiated via QEMU on the same network. In this server, a network card provides connectivity to the environment and the other network card receives all network traffic for analysis by the Monitoring Environment. Getting traffic to Suricata is done by Ettercap. The desktop has two virtual machines instantiated via Oracle VirtualBox, on the same network and acts on the network as a client as well.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-06-06 to 2019-01-31</p> <p> </p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Internet environment
<p>We have for the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-08-28 to 2019-11-14</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Environment with scalability
<p>We have used the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server.</p> <p>We added 08 virtual machines and performed the following test with a denial of service DoS attack:</p> <p>01 virtual machine from 04:00 pm to 23:55 pm on 2019-12-04 with an interval every 01 hour;<br> 02 virtual machines from 23:55 am on 2019-12-04 to 08:50 am on 2019-12-05 with an interval every 01 hour;<br> 04 virtual machines as of 08:55 am on 2019-12-05 to 05:25 pm on 2019-12-06 with an interval every 5 minutes;<br> 08 virtual machines from 05:30 pm on 2019-12-06 to 23:59 on 2019-12-06 with an interval every 5 minutes;<br> End of tests with shutdown of virtual machines at 23:59 on 2019-12-06.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2019-12-04 to 2019-12-06</p>
SINS database - Node 4 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 7 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 6 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 12 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 3 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://kuleuvenadvise.github.io/SINS_database/">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 13 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 10 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 9 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 2 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 8 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 11 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Automatic Generation of Explanations in Autonomous Systems: Enhancing Human Interaction in Smart Home Environments
<p>The file named “Dataset” is the generation of scenarios and explanations used for the proposal.<br>The file named “Questionnaire answers & data analysis” corresponds to the application of a questionnaire addressed to 118 people.</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
SINS database - Node 1 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://kuleuvenadvise.github.io/SINS_database/">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
New Ulm at HOME (Healthy Offerings Via the Mealtime Environment), NU-HOME
ClinicalTrials.gov study NCT02973815. IPD Sharing: NO. Countries: 1. Publications: 7.
ScienceDex guides
Understand access before you commit
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.