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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>
FIGURE 5. Justicia zapoteca. A. Shoot with inflorescence. B. Leaf. C. Inflorescence node with flower. D. Bracteole. E. Bract. F. Calyx. G in New and Reconsidered Mexican Acanthaceae XII
FIGURE 5. Justicia zapoteca. A. Shoot with inflorescence. B. Leaf. C. Inflorescence node with flower. D. Bracteole. E. Bract. F. Calyx. G. Distal portion of stamen with bithecous anther. H. Capsule. I. Seed. (A, C from Daniel et al. 11898; B, H, I from Daniel et al. 11810; D–G from Saynes V. & Elorsa 2741). Drawn by Sean Edgerton.
NYC Bike Sharing Network: Time-Series Enhanced Nodes and Edges Dataset
<p>This dataset presents a comprehensive graph representation of the New York City Bike Sharing system, structured with nodes representing stations and edges delineating trips between these stations. The dataset is distinctive in integrating dynamic properties as time series data, which are meticulously updated using historical records (csv files) and live data feeds (gbfs files) provided by<a href="https://citibikenyc.com/system-data" target="_blank" rel="noopener"> NYC Bike sharing system</a>. </p> <ul> <li> <p><strong>Nodes</strong>:</p> <ul> <li><strong>Source</strong>: Data is collected from the New York City Bike Station Information API.</li> <li><strong>Attributes</strong>: <ul> <li><strong>ID</strong>: Unique identifier for each station.</li> <li><strong>Name</strong>: Name of the station.</li> <li><strong>Capacity</strong>: Number of bikes the station can accommodate.</li> <li><strong>Short ID</strong>: A condensed identifier used internally.</li> </ul> </li> <li><strong>Time Series Data</strong>: <ul> <li>Updated every 5 minutes from the Station Status API.</li> <li>Captures changes in bike availability, recording values only when they differ from previous data points.</li> </ul> </li> </ul> </li> <li> <p><strong>Edges</strong>:</p> <ul> <li><strong>Source</strong>: Compiled from trip data provided in CSV format specific to NYC Bike Sharing.</li> <li><strong>Attributes</strong>: <ul> <li><strong>Trip Counter</strong>: Total number of trips recorded.</li> <li><strong>Bike Type Counter</strong>: Counts trips made with electric versus classic bikes.</li> <li><strong>Trip Type Counter</strong>: Separates trips made by members versus casual riders.</li> <li><strong>Active Trips Tracker</strong>: Tracks the number of active trips at any given moment.</li> </ul> </li> <li><strong>Aggregation</strong>: Trip data between identical start and end points, in the same direction, are aggregated into a single edge, with time-series tracking the frequency of these trips.</li> </ul> </li> </ul>
Italian TEN-T road network and Hydrogen Refueling Station nodes
<p>This folder contains three ESRI-shapefiles (representing respectively the network edges, the network nodes and the resulting positions found with an optimization framework to site hydrogen refueling stations (HRS) in Italy) and a CSV-file used to characterize the vehicle traffic flow on each origin-destination path on the represented network. </p> <p><strong>network_nodes.shp</strong></p> <p>There are different type of nodes:</p> <ul> <li>NUTS-3 nodes, being the possible origin or destination nodes. They represent the weighted centroid of the NUTS-3 region they refer to. </li> <li>Fuel areas centroids: obtained as the centroids of the dissolved buffer areas of the conventional fuel stations on the road network.</li> <li>Intersection nodes: manually added on the network nodes intersections.</li> <li>Main road entrances.</li> <li>Port nodes.</li> </ul> <p><strong>network_edges.shp</strong></p> <p>The edges represent the Trans-European Network (TEN-T) core and comprehensive layers. Some edges are manually added:</p> <ul> <li>Edges connecting NUTS-3 nodes to the main road network;</li> <li>Ferry edges, which have "road length" equal to 0, as they do not imply fuel consumption for road trucks.</li> </ul> <p><strong>italian_paths_OD_NUTS3_vR1.csv</strong></p> <p>Containing O-D paths thorugh the TEN-T road network from each origin to each destination. Each path is represented as an ordered succession of nodes and of their positions with respect to the origin node. For each path an estimation of the tons and trucks expected on it by 2030 is given. </p> <p><strong>HRS_nodes.shp</strong></p> <p>HRS nodes position and specifications obtained with an optimization framework to serve the national freight transport system if vehicles paths with dist_ROAD > 100 km, Traffic_flow_trucks_2030 >= 2600, initial vehicle range = 300 and maximum vehicle range = 600 km are considered. The fuel cell hydrogen electric vehicles share over the total number of circulating vehicles is assumed equal to 10%. </p> <p> </p> <p> </p>
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50). in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50).
Figure 9. Mapped and reconstructed soldier head shapes positioned across a Termitidae phylogeny. Nodes a–g in Termite soldier defence strategies: a reassessment of Prestwich's classification and an examination of the evolution of defence morphology using extended eigenshape analyses of head morphology
Figure 9. Mapped and reconstructed soldier head shapes positioned across a Termitidae phylogeny. Nodes a–g are referred to in the text.
Marconi supercomputer KNL partition benchmark (HPL and single node stream)
<p>This dataset collect the data stored during the procedure of evaluation of the Marconi KNL machine at CINECA in order to classify it for Top500 list.</p> <p>Dataset also include a report summarizing the results of benchmarks (STREAM for single node memory assessment and HPL for HPC parallel performance) carried out in Nov. 2016.</p>
Text-fig. 8. Ammonoids Melvilloceras rotaii (LIBROVITCH in POPOV, 1979) from the Mospyne Formation. a, b: A slightly laterally compressed specimen IGSU-7/8040, lateral view (a), ventral view (b). c, d: A slightly laterally compressed specimen IGSU-7/472, lateral view (c), ventral view (d). e, f: Specimen IGSU-7/540, lateral view (e), ventral view (f). g–i: Specimen IGSU-7/468, dorsal view (g), lateral view (h), ventral view (i). j: Specimen IGSU-7/6538, dorsal view. k, l: Specimen IGSU-7/620, lateral view (k), ventral view (l). m–q: Lateral views of the conchs showing the different relief of the umbilical nodes; m – specimen IGSU-4/4148, n – specimen IGSU-7/522, o – specimen IGSU-7/468a, p – specimen IGSU-7/472a, q – specimen IGSU-7/316. Scale bars 10 mm. in Late Bashkirian Ammonoids From The Mospyne Formation Of The Donets Basin, Ukraine
Text-fig. 8. Ammonoids Melvilloceras rotaii (LIBROVITCH in POPOV, 1979) from the Mospyne Formation. a, b: A slightly laterally compressed specimen IGSU-7/8040, lateral view (a), ventral view (b). c, d: A slightly laterally compressed specimen IGSU-7/472, lateral view (c), ventral view (d). e, f: Specimen IGSU-7/540, lateral view (e), ventral view (f). g–i: Specimen IGSU-7/468, dorsal view (g), lateral view (h), ventral view (i). j: Specimen IGSU-7/6538, dorsal view. k, l: Specimen IGSU-7/620, lateral view (k), ventral view (l). m–q: Lateral views of the conchs showing the different relief of the umbilical nodes; m – specimen IGSU-4/4148, n – specimen IGSU-7/522, o – specimen IGSU-7/468a, p – specimen IGSU-7/472a, q – specimen IGSU-7/316. Scale bars 10 mm.
Companion data of Summarizing task-based applications behavior over many nodes through progression clustering
<p>This is the companion data for the paper *Summarizing task-based applications behavior over many nodes through progression clustering* by Lucas Leandro Nesi, Vinícius Garcia Pinto, Lucas Mello Schnorr, and Arnaud Legrand accept for publication in 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (<a href="https://www.pdp2023.org/">PDP 2023</a>). The remaining of the companion is at: https://gitlab.com/lnesi/companion-pdp-2023.</p>
Supplementary Material 7 including Salamandra salamandra occurrence data, topographic, geological and land cover data and node-based resistances
<p>Supplementary material for the article "Habitat connectivity supports the local abundance of fire salamanders (Salamandra salamandra) but also the spread of Batrachochytrium salamandrivorans" by Bolte <em>et al</em>. (2023) published in Landscape Ecology (DOI: 10.1007/s10980-023-01636-8)</p> <p>This folder comprises a .shp file with fire salamander occurrences, topographic and land cover data (GeoTiff) from the northern Eifel region as well as the R Code used for the statistical analysis of salamander habitat suitability and connectivity.</p>
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)
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.