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5 results for “multimodal contexts”
ROCOv2: Radiology Objects in COntext Version 2, An Updated Multimodal Image Dataset
<p>Recent advances in deep learning techniques have enabled the development of systems for automatic analysis of medical images. These systems often require large amounts of training data with high quality labels, which is difficult and time consuming to generate.</p> <p>Here, we introduce Radiology Object in COntext Version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PubMed Open Access subset. Concepts for clinical modality, anatomy (X-ray), and directionality (X-ray) were manually curated and additionally evaluated by a radiologist. Unlike MIMIC-CXR, ROCOv2 includes seven different clinical modalities.</p> <p>It is an updated version of the ROCO dataset published in 2018, and includes 35,705 new images added to PubMed since 2018, as well as manually curated medical concepts for modality, body region (X-ray) and directionality (X-ray). The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical 2023. The participants had access to the training and validation sets after signing a user agreement.</p> <p>The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using the UMLS concepts provided with each image, e.g., to build systems to support structured medical reporting.</p> <p>Additional possible use cases for the ROCOv2 dataset include the pre-training of models for the medical domain, and the evaluation evaluation of deep learning models for multi-task learning.</p>
Context-dependent multimodal behaviour in a coral reef fish: Stage 1 & 2 total duration and count data in behaviour trials
<p>Animals are expected to respond flexibly to changing circumstances, with multimodal signalling providing potential plasticity in social interactions. Whilst numerous studies have documented context-dependent behavioural trade-offs in terrestrial species, far less work has considered such decision-making in fish, especially in natural conditions. Coral reef ecosystems host 25% of all known marine species, making them hotbeds of competition and predation. We conducted experiments with wild Ambon damselfish (<em>Pomacentrus amboinensis)</em> to investigate context-dependent responses to a conspecific intruder; specifically, how nest defence is influenced by an elevated predation risk. We found that nest-defending male Ambon damselfish responded aggressively to a conspecific intruder, spending less time sheltering and more time interacting, as well as signalling both visually and acoustically. In the presence of a model predator compared to a model herbivore, males spent less time interacting with the intruder, with a tendency towards reduced investment in visual displays compensated for by an increase in acoustic signalling instead. We therefore provide ecologically valid evidence that the context experienced by an individual can affect its behavioural responses and multimodal displays towards conspecific threats.</p>
Context-dependent multimodal behaviour in a coral reef fish: Stage 1 & 2 total duration and count data in behaviour trials
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PiH Dataset for Determining Exception Context in Assembly Operations form Multimodal Data
<p>PiH Dataset used in Simonič, M.; Majcen Hrovat, M.; Džeroski, S.; Ude, A.; Nemec, B. Determining Exception Context in Assembly Operations from Multimodal Data. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 7962. https://doi.org/10.3390/s22207962</p> <p>The dataset consists of color images of different outcomes of the PiH task as well as the corresponding Cartesian pose of the robot end-effector and force torque data. Data is organized into two folders, representing one of the two possible insertion slots. In each of the folders, data is further split into the following cases:<br> - error in insertion target position ranging from -10 to 10 mm in x direction in 1 mm steps,<br> - error in insertion target position ranging from -10 to 10 mm in y direction in 1 mm steps,<br> - no positional error.</p> <p>Multiple attempts were made for each case.</p> <p>Each entry has unique date-time tag and comprises: RGB image (.jpg) and .mat file with <em>states </em>object that contains reference and measured target pose in Cartesian space (positions and quaternions) and raw force-torque sensor data and force-torque data transformed to the tool frame.</p> <p>The experiments were performed with Franka Emika Panda collaborative robot. For acquisition of image data an Intel Realsense D435 RGB-D camera has been utilized. </p>
Rings Insertion Dataset for Determining Exception Context in Assembly Operations form Multimodal Data
<p>Rings Insertion Dataset used in Simonič, M.; Majcen Hrovat, M.; Džeroski, S.; Ude, A.; Nemec, B. Determining Exception Context in Assembly Operations from Multimodal Data. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 7962. https://doi.org/10.3390/s22207962</p> <p>The dataset consists of color images of different outcomes of the ring insertion task as well as the corresponding Cartesian pose of the robot end-effector and force torque data. Data is organized into four folders, representing one of the possible insertion slots. In each of the folders, data is further split into the following cases:<br> - error in insertion target position ranging from -3 to 3 mm in x direction in 1 mm steps,<br> - error in insertion target position ranging from -3 to 3 mm in y direction in 1 mm steps,<br> - no positional error,<br> - other unidentified error (bad insertion).</p> <p>Multiple attempts were made for each case.</p> <p>Each entry has unique date-time tag and comprises: RGB image (.jpg) and .mat file with <em>states </em>object that contains reference and measured target pose in Cartesian space (positions and quaternions) and raw force-torque sensor data and force-torque data transformed to the tool frame.</p> <p>The experiments were performed with Franka Emika Panda collaborative robot. For acquisition of image data an Intel Realsense D435 RGB-D camera has been utilized. </p>
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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.