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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>
Data for: Holocene sedimentary record and coastal evolution in the Makran subduction zone (Iran)
<p>The data comes from the study of Holocene beaches from the Iranian Makran. More exactly, the beaches in Pozm bay, Chabahar bay and the longitudinal beach west of the village of Beris. The data contain field pictures and their legends, results and analytical details of radiocarbon dating, SEM secondary electron images of the analysed shells and the results and analytical details of optically stimulated luminescence dating (OSL).<br> The data is published in support for the paper mentioned in the title.</p> <p>A to D are field pictures and legends of:<br> A - Pozm bay field pictures<br> B - Chabahar bay field pictures<br> C - Beris beach field pictures<br> D - Observed current depositional settings</p> <p>E - Radiocarbon dating results and analytical supplementary information<br> F - Secondary electrons SEM images of the analysed shells</p> <p>G - OSL dating results and analytical supplementary information</p> <p>Radiocarbon analytical details are provided, together with XRD analysis of the Aragonitic shells, SEM secondary electron images of analysed shells and calibration details. Method details are in the paper.</p> <p>OSL results are provided with the analytical details, such as: Environmental dose parameters, OSL raw measurements (out of the machine), OSL Histograms and Dose-Recovery tests. Method details are in the paper.</p>
Fig 9. Top 10 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 9. Top 10 of most utilized journals from 1946 to 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 6 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 6 (continued from previous page). Number of total articles and co-authored articles from 1946 to 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 6 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 6 (continued on next page). Number of total articles and co-authored articles from 1946 to 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 5 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 5 (continued on next page). Number of articles published by continent (Europe, North America, South America, Africa, Asia and Australia) between 1946 and 2012. A. Cicadellidae. B. Miridae. C. Pyralidae. D. Staphylinidae.
Fig 2 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 2. Average new species described per article (Cicadellidae, Miridae, Pyralidae and Staphylinidae combined) from 1946 to 2012.
Fig. 10 in New marine Thinophilus species (Diptera: Dolichopodidae: Hydrophorinae) from the Thai Andaman Sea coast and new records from peninsular Thailand
Fig. 10. Thinophilus subapicalis Samoh & Grootaert sp. nov. Male terminalia (Abdulloh Samoh leg., PSUNHM (PSUZCDIP0015)). A. Ventral view of hypopygium. B. Lateral view. C. Dorsal view of cerci. Scale = 0.1 mm.
Fig 1 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 1. Time series of newly described species for the families Cicadellidae, Miridae, Pyralidae and Staphylinidae between 1946 and 2012. A. Number of new species. B. Number of articles with new species.
Fig. 9 in New marine Thinophilus species (Diptera: Dolichopodidae: Hydrophorinae) from the Thai Andaman Sea coast and new records from peninsular Thailand
Fig. 9. Thinophilus subapicalis Samoh & Grootaert sp. nov. Female habitus (Abdulloh Samoh leg., PSUNHM (PSUZCDIP0016)).
Fig. 8 in New marine Thinophilus species (Diptera: Dolichopodidae: Hydrophorinae) from the Thai Andaman Sea coast and new records from peninsular Thailand
Fig. 8. Thinophilus subapicalis Samoh & Grootaert sp. nov. Male habitus (Abdulloh Samoh leg., PSUNHM (PSUZCDIP0015)).
Fig 4 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 4. Total and average article length for for papers on the four families of Cicadellidae, Miridae, Pyralidae and Staphylinidae between 1946 and 2012.
Fig 3 in Publishing trends and productivity in insect taxonomy from 1946 through 2012 based on an analysis of the Zoological Record for four species-rich families
Fig 3. Average page length of new species descriptions (Cicadellidae, Miridae, Pyralidae and Staphylinidae combined) between 1946 and 2012.
Fig. 6 in New marine Thinophilus species (Diptera: Dolichopodidae: Hydrophorinae) from the Thai Andaman Sea coast and new records from peninsular Thailand
Fig. 6. Thinophilus plektron Samoh & Grootaert sp. nov. Female habitus (Abdulloh Samoh leg., PSUNHM (PSUZCDIP0014)).
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.