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OLVSL_ Object-location visual statistical learning
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Study of Calabrian Sounding Objects. Interviews and Field Notes - Festa della Pita
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - G. Orlando
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Interview and Field Notes - G. Guidoccio
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - S. Trunzo
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - S. Mancini
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Interview and Field Notes - G. Vaccaro
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
ELKI Multi-View Clustering Data Sets Based on the Amsterdam Library of Object Images (ALOI)
<p>These data sets were originally created for the following publications:</p> <p><em>M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek</em><br> <strong>Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?</strong><br> In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany, 2010.</p> <p><em>H.-P. Kriegel, E. Schubert, A. Zimek</em><br> <strong>Evaluation of Multiple Clustering Solutions</strong><br> In 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece, 2011.</p> <p>The outlier data set versions were introduced in:</p> <p><em>E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel</em><br> <strong>On Evaluation of Outlier Rankings and Outlier Scores</strong><br> In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.</p> <p> </p> <p>They are derived from the original image data available at <a href="https://aloi.science.uva.nl/">https://aloi.science.uva.nl/</a></p> <p>The image acquisition process is documented in the original ALOI work: <em>J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders</em>, <strong>The Amsterdam library of object images</strong>, Int. J. Comput. Vision, 61(1), 103-112, January, 2005</p> <p>Additional information is available at: <a href="https://elki-project.github.io/datasets/multi_view">https://elki-project.github.io/datasets/multi_view</a></p> <p>The following views are currently available:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>Object number</td> <td>Sparse 1000 dimensional vectors that give the <em>true</em> object assignment</td> <td><a href="6355684/files/objs.arff.gz">objs.arff.gz</a></td> </tr> <tr> <td>RGB color histograms</td> <td>Standard RGB color histograms (uniform binning)</td> <td><a href="6355684/files/aloi-8d.csv.gz">aloi-8d.csv.gz</a> <a href="6355684/files/aloi-27d.csv.gz">aloi-27d.csv.gz</a> <a href="6355684/files/aloi-64d.csv.gz">aloi-64d.csv.gz</a> <a href="6355684/files/aloi-125d.csv.gz">aloi-125d.csv.gz</a> <a href="6355684/files/aloi-216d.csv.gz">aloi-216d.csv.gz</a> <a href="6355684/files/aloi-343d.csv.gz">aloi-343d.csv.gz</a> <a href="6355684/files/aloi-512d.csv.gz">aloi-512d.csv.gz</a> <a href="6355684/files/aloi-729d.csv.gz">aloi-729d.csv.gz</a> <a href="6355684/files/aloi-1000d.csv.gz">aloi-1000d.csv.gz</a></td> </tr> <tr> <td>HSV color histograms</td> <td>Standard HSV/HSB color histograms in various binnings</td> <td><a href="6355684/files/aloi-hsb-2x2x2.csv.gz">aloi-hsb-2x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-3x3x3.csv.gz">aloi-hsb-3x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-4x4x4.csv.gz">aloi-hsb-4x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-5x5x5.csv.gz">aloi-hsb-5x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-6x6x6.csv.gz">aloi-hsb-6x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-7x7x7.csv.gz">aloi-hsb-7x7x7.csv.gz</a> <a href="6355684/files/aloi-hsb-7x2x2.csv.gz">aloi-hsb-7x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-7x3x3.csv.gz">aloi-hsb-7x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-14x3x3.csv.gz">aloi-hsb-14x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-8x4x4.csv.gz">aloi-hsb-8x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-9x5x5.csv.gz">aloi-hsb-9x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-13x4x4.csv.gz">aloi-hsb-13x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-14x5x5.csv.gz">aloi-hsb-14x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-10x6x6.csv.gz">aloi-hsb-10x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-14x6x6.csv.gz">aloi-hsb-14x6x6.csv.gz</a></td> </tr> <tr> <td>Color similiarity</td> <td>Average similarity to 77 reference colors (not histograms) 18 colors x 2 sat x 2 bri + 5 grey values (incl. white, black)</td> <td><a href="6355684/files/aloi-colorsim77.arff.gz">aloi-colorsim77.arff.gz</a> (feature subsets are meaningful here, as these features are computed independently of each other)</td> </tr> <tr> <td>Haralick features</td> <td>First 13 Haralick features (radius 1 pixel)</td> <td><a href="6355684/files/aloi-haralick-1.csv.gz">aloi-haralick-1.csv.gz</a></td> </tr> <tr> <td>Front to back</td> <td>Vectors representing front face vs. back faces of individual objects</td> <td><a href="6355684/files/front.arff.gz">front.arff.gz</a></td> </tr> <tr> <td>Basic light</td> <td>Vectors indicating basic light situations</td> <td><a href="6355684/files/light.arff.gz">light.arff.gz</a></td> </tr> <tr> <td>Manual annotations</td> <td>Manually annotated object groups of semantically related objects such as cups</td> <td><a href="6355684/files/manual1.arff.gz">manual1.arff.gz</a></td> </tr> </tbody></table> <p><strong>Outlier Detection Versions</strong></p> <p>Additionally, we generated a number of subsets for outlier detection:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>RGB Histograms</td> <td>Downsampled to 100000 objects (553 outliers)</td> <td><a href="6355684/files/aloi-27d-100000-max10-tot553.csv.gz">aloi-27d-100000-max10-tot553.csv.gz</a> <a href="6355684/files/aloi-64d-100000-max10-tot553.csv.gz">aloi-64d-100000-max10-tot553.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 75000 objects (717 outliers)</td> <td><a href="6355684/files/aloi-27d-75000-max4-tot717.csv.gz">aloi-27d-75000-max4-tot717.csv.gz</a> <a href="6355684/files/aloi-64d-75000-max4-tot717.csv.gz">aloi-64d-75000-max4-tot717.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 50000 objects (1508 outliers)</td> <td><a href="6355684/files/aloi-27d-50000-max5-tot1508.csv.gz">aloi-27d-50000-max5-tot1508.csv.gz</a> <a href="6355684/files/aloi-64d-50000-max5-tot1508.csv.gz">aloi-64d-50000-max5-tot1508.csv.gz</a></td> </tr> </tbody></table>
Long-term Memory (LTM) for famous Faces, Places, and common Objects
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Action-related object pairs - fMRI dataset
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Data to "Predicting precision grip grasp locations on three-dimensional objects"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Klein, L. K. ^, Maiello, G. ^, Paulun, V. C., & Fleming, R. W. (in press). <br> Predicting precision grip grasp locations on three-dimensional objects. PLOS Computational Biology<br> ^co-first authors </p> <p>A preprint version of the manuscript is currently available at: https://doi.org/10.1101/476176</p>
Smartbay Marine Species Object Detection Training dataset
<h1>Training dataset</h1> <p>The SmartBay Observatory in Galway Bay is an important contribution by Ireland to the growing global network of real-time data capture systems deployed within the ocean – technology giving us new insights into the ocean which we have not had before.</p> <p>The observatory was installed on the seafloor 1.5km off the coast of Spiddal, County Galway, Ireland . The observatory uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. This observatory equipment allows ocean researchers unique real-time access to monitor ongoing changes in the marine environment. Data relating to the marine environment at the site is transferred in real-time from the SmartBay Observatory through a fibre optic telecommunications cable to the Marine Institute headquarters and onwards onto the internet. The data includes a live video stream, the depth of the observatory node, the sea temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water.</p> <p>The Smartbay Marine Species Object Detection training Dataset is an initial Bounding Box Annotated image dataset used in attempting to Train a YOLOv8 Object Detection Model to classify the Marine Fauna observed in the Smartbay Observatory Video footage using species names.</p> <p>The imagery used in this training dataset consists of image frame captures from the <a href="https://smartbay.marine.ie">Smartbay</a> video Archive files, CC-BY imagery from the <a href="https://www.minka-sdg.org">www.minka-sdg.org</a> website and images taken by Eva Cullen in the "<a href="https://nationalaquarium.ie/">Galway Atlantaquaria</a>" Aquarium in Galway, Ireland.</p> <p>The imagery were annotated using CVAT, collated on <a href="https://www.roboflow.com/">Roboflow</a> and exported in YOLOv8 training dataset format. </p>
Smartbay Marine Types Object Detection Training dataset
<h1>Training Dataset</h1> <p>The SmartBay Observatory in Galway Bay is an important contribution by Ireland to the growing global network of real-time data capture systems deployed within the ocean – technology giving us new insights into the ocean which we have not had before.</p> <p>The observatory was installed on the seafloor 1.5km off the coast of Spiddal, County Galway, Ireland . The observatory uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. This observatory equipment allows ocean researchers unique real-time access to monitor ongoing changes in the marine environment. Data relating to the marine environment at the site is transferred in real-time from the SmartBay Observatory through a fibre optic telecommunications cable to the Marine Institute headquarters and onwards onto the internet. The data includes a live video stream, the depth of the observatory node, the sea temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water.</p> <p>The Smartbay Marine Types Object Detection training Dataset is an initial Bounding Box Annotated image dataset used in attempting to Train a YOLOv8 Object Detection Model to classify the Marine Fauna observed in the Smartbay Observatory Video footage using broad "Marine Type" classes.</p> <p>The imagery used in this training dataset consists of image frame captures from the <a href="https://smartbay.marine.ie">Smartbay</a> video Archive files, CC-BY imagery from the <a href="https://www.minka-sdg.org">www.minka-sdg.org</a> website and images taken by Eva Cullen in the "<a href="https://nationalaquarium.ie/">Galway Atlantaquaria</a>" Aquarium in Galway, Ireland.</p> <p>The imagery were annotated using CVAT, collated on <a href="https://www.roboflow.com/">Roboflow</a> and exported in YOLOv8 trainign dataset format. </p>
Study of Calabrian Sounding Objects. Interview and Field Notes - A. Vescio & G. Lucia
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Interview and Field Notes - A. Trunzo, S. Trunzo and T. Vaccaro
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - E. Ruperto
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Interview and Field Notes - E. Curcio
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Interview and Field Notes - C. Macchione
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - D. Mastroianni
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Patterns of object play behaviour and its functional implications in free-flying ravens (supplementary data)
<p>This resource contains the processed data sets and R scripts associated with the article titled "Patterns of object play behaviour and its functional implications in free-flying ravens" authored by Awani Bapat, Anna E Kempf, Salome Friry, Palmyre H Boucherie, Thomas Bugnyar, published in Scientific Reports on 02-01-2025.</p>
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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.