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901 results for “Modality”
Data for Wind Energy Science paper "Modal dynamics of structures with bladed isotropic rotors and its complexity for 2-bladed rotors"
<p>The files are data files with model input and Matlab files with model parameters and function that sets up the block matrices of the dynamic model used in the paper. The Matlab script "test_repo.m" shows how to this function with all input.</p>
mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
<p>The ability to estimate 3D human body pose and movement, also known as human pose estimation~(HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few datasets exploit multiple modalities and focus on home-based health monitoring.</p> <p>To bridge this gap, we present <em>mRI</em>, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 5 million frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality.</p> <p>We hope that the release of <em>mRI</em> can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly, facilitate the applications of home-based health monitoring.</p>
Multi-modal pose estimation in XR applications leveraging integrated sensing and communication: Dataset
<p>This dataset refers to paper Multi-modal pose estimation in XR applications leveraging integrated sensing and communication in workshop of ACM Mobicom. CSI of 3 people performing a set of 8 poses. This dataset contain discrete classes and corresponding CSI data. Kinect poses can be found here (https://github.com/nisarnabeel/multi-modal-pose-estimation-CSI-mmWave).</p> <p> </p> <p>Abstract: Mobile extended reality (XR) applications are anticipated to generate substantial traffic for 6G. Such applications not only require high data rate and low-latency transmissions, but also accurate and real-time pose estimation to enable interactive and immersive experiences. While sub-6 GHz signals have been exploited for pose estimation, they cannot cope up with multi-gigabit data rates required by XR applications. Instead, mobile communications at mmWave frequencies can potentially support data rates up to several giga-bits per second (Gbps) and, therefore, can be used to deliver XR content wirelessly to the Head-Mounted Display (HMD). Moreover, mmWave frequencies can offer improved sensing due to the large available bandwidth. Therefore, mmWave communications can play a crucial role in enabling device-free interactivity by offering both high-speed communication and accurate sensing capabilities. However, mmWave propagation characteristics are different from sub-6 GHz. Path loss plays a significant role, and can lead to degraded sensing performance. Therefore, our proposal supplements wireless sensing at mmWave frequencies with wireless electromyography (EMG) armbands. By capturing patterns of muscle activities, we can counteract the limitations of mmWave-based pose estimation, thereby enriching the granularity and precision of pose estimation. This paper proposes a conceptual architecture to achieve multi-modal pose estimation for XR applications. Early results highlight the shortcomings of mmWave-based sensing, and we identify future steps and opportunities on integration of both approaches.</p>
Multi-modal phantom experiments, mimicking flow through the mitral heart valve
<p>In this repository all experimental data obtained from a phantom study on the left heart, including a deformable mitral valve, is reported. Using invasive catheter pressure measurements, magnetic resonance imaging and ultrasound imaging, several parameters relevant to diagnosing heart valve disease were measured on the phantom. The data is reported in MS excel files (xlsx format) and the geometry of the phantom is reported in STL and STEP file format, together with an exploded view, indicating how the parts are put together.</p>
Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells
<p> </p><p>Datasets to go along with the publication listed:</p><p>full_object.rds: Brain Organoid Phospho-Seq dataset with ATAC, Protein and imputed RNA data</p><p>rna_object.rds: Reference whole cell scRNA-Seq object on Brain organoids</p><p>multiome_object.rds: Bridge dataset containing RNA and ATAC modalities for Brain organoids</p><p>metacell_allnorm.rds: Metacell object for finding gene-peak-protein linkages in Brain organoid dataset</p><p>fullobject_fragments.tsv.gz: fragment file to go with the full object</p><p>fullobject_fragments.tsv.gz.tbi:index file for the full object fragment file</p><p>multiome_fragments.tsv.gz: fragment file to go with the multiome object</p><p>multiome_fragments.tsv.gz.tbi:index file for the multiome object fragment file</p><p>K562_Stem.rds : object corresponding to the pilot experiment including K562 cells and iPS cells</p><p>K562_stem_fragments.tsv.gz: fragment file to go with the K562_stem object</p><p>K562_stem_fragments.tsv.gz.tbi: index file for the K562_stem object fragment file</p><p>retina.rds : object corresponding to the retinal organoid phospho-seq experiment</p><p>retina_fragments.tsv.gz: fragment file to go with the retina object</p><p>retina_fragments.tsv.gz.tbi: index file for the retina object fragment file</p><p>retina_multi.rds : object corresponding to the retinal organoid phospho-seq-multiome experiment</p><p>retina_multi_fragments.tsv.gz: fragment file to go with the retina_multi object</p><p>retina_multi_fragments.tsv.gz.tbi: index file for the retina_multi object fragment file</p><p>To use the K562, multiome, retina and retina_multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the K562 and multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the "fullobject" dataset provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file: </p><p>#load the stringr package library(stringr) #load the object obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL #Remove unwanted residual information and rename cells obj@reductions$norm.adt.pca <- NULL obj@reductions$norm.pca <- NULL obj <- RenameCells(obj, new.names = str_remove(Cells(obj), "atac_")) # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p> </p><p> </p>
Effect of the red uniform on the judgment of position or movement used in Wushu Routine, evaluated by practitioners of the modality
<p>Experiment 1 design was adopted a 2 (Color: red, blue) x 2 (Gender: male, female) ANOVA. 144 Participants were randomly assigned to four groups based on uniform color (red vs. blue) and practitioners' gender (male vs. female) in a 2 x 2 between-subject design according to the method of random number table. There were 36 male athletes and 36 female athletes in the red uniform group, and 36 male athletes and 36 female athletes in the blue uniform group. Experiment 2 used within-subjects design to conducted experiment separately for the blue and red groups. The female practitioners were randomly assigned to two groups according to the method of random number table and made ratings of the athletes once during the ovulation phase and once during the non-ovulation phase. The order in which the tasks were completed was balanced between subjects: Half of the females in the blue group were tested first during ovulation and then during non-ovulation; the other half were tested first during non-ovulation and then during ovulation. The red group did the same arrangement. They were instructed to look at each photograph for 5s and then rate the quality of the each athlete's movements. After scoring was completed, they were told to proceed to the questionnaire, which was designed to probe for awareness of the manipulation. Finally, look at the differences in the ratings of Wushu Routine athletes wearing different colored uniform by these different groups of participants, and whether the subjects in Experiment 1 would have been aware of color effect.</p><p>The results of Experiment 1 showed that both male and female athletes wearing red uniform (compared to blue uniform) received higher ratings, and the red effect was especially strong when male practitioners rated female athletes. The results of Experiment 2, in an all-female sample, showed that in most cases there was no difference in ratings made by women in the ovulation and non-ovulation phases of their menstrual cycle, with the exception of their ratings of male athletes wearing red; in this condition, women gave higher ratings when they were in the ovulation phase of their cycle.</p>
Two storyboards on modal meanings in Kannada
<p>This dataset contains recordings in the Dravidian language Kannada of two storyboards targeting modal expressions: </p><ol><li>Matthew Rolka & Seth Cable (2014). Tom and Mittens. <i>Totem Field Storyboards </i>. Retrieved from http://www.totemfieldstoryboards.org on Nov 29, 2023.</li><li>TFS Working Group (2011). Chore Girl. Retrieved from http://www.totemfieldstoryboards.org on Nov 29, 2023.</li></ol><p>The bundle contains the audio files and transcriptions in ELAN. The annotations also contain partial translations and some glosses.</p><p>These files were created in 2023 as part of the class "Urban linguistic fieldwork" at Heinrich-Heine-Universität Düsseldorf, led by Kilu von Prince and Niklas Wiskandt.</p><p>The speaker is Akhilesh Ramarao, who also supplied information about translations and grammatical features. The other creators are the students who worked with Akhilesh on the annotations.</p><p>These files may be useful to people working on Kannada, and on the typology and semantics of modal expressions.</p>
A Golden Sword Modal
***This Avonthian Sword was Rendered in Eevee Rendered. It Was Exported in Ply, x3d ,Mtl, glb, fbx, abc,Obj, Stl, dae ,usdc Formats. This Sword Modelled In Blender Version 2.73.It Has Golden Handle.*** Source: Objaverse 1.0 / Sketchfab
Data from: Dissecting abstract, modality-specific and experience-dependent coding of affect in the human brain
<p>Emotion and perception are tightly intertwined, as affective experiences often arise from the appraisal of sensory information. Nonetheless, whether the brain encodes emotional instances using a sensory-specific code or in a more abstract manner is unclear. Here, we answer this question by measuring the association between emotion ratings collected during a unisensory or multisensory presentation of a full-length movie and brain activity recorded in typically-developed, congenitally blind and congenitally deaf participants. Emotional instances are encoded in a vast network encompassing sensory, prefrontal, and temporal cortices. Within this network, the ventromedial prefrontal cortex stores a categorical representation of emotion independent of modality and previous sensory experience, and the posterior superior temporal cortex maps the valence dimension using an abstract code. Sensory experience more than modality impacts how the brain organizes emotional information outside supramodal regions, suggesting the existence of a scaffold for the representation of emotional states where sensory inputs during development shape its functioning.</p>
Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction
<p><strong>Data</strong></p> <ul> <li><strong>22-12-05-data</strong>: original molecular XANES data created from <a href="https://doi.org/10.1103/PhysRevResearch.5.013180">Ghose <em>et al.</em></a></li> <li><strong>23-04-26-ml-data</strong>: machine learning-ready data which is prepared in the format required by <a href="https://github.com/matthewcarbone/Crescendo">Crescendo</a>.</li> <li><strong>23-05-03-hp</strong>: hyper-parameter tuning results from 23-04-26-ml-data.</li> <li><strong>23-05-05-ensembles</strong>: ensemble results from 23-04-26-ml-data.</li> <li><strong>23-05-11-ml-data-CUTOFF8</strong>: a special machine learning-ready dataset constructed by a unique partitioning: only molecules with less than or equal to 8 atoms/molecule are used for training/validation, the rest are used for testing.</li> <li><strong>23-12-06_torch_models</strong>: torch only models which can be easily used independently of our ML helper repository, Crescendo. Instead, it can be used with a few lines of code found in multimodal_molecules/core.py, in our <a href="https://github.com/AI-multimodal/multimodal-molecules">GitHub respository</a>.</li> </ul> <p><strong>Funding</strong></p> <p>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704.</p>
Multi-modal User Interactions for Recommendations
<p>This repository contains the data for <a href="https://doi.org/10.1145/3626772.3657881">Dataset and Models for Item Recommendation Using Multi-Modal User Interactions</a>.</p> <p>We publish a real-world dataset from the insurance domain with multi-modal user interactions that can be used in recommendation models. The dataset is anonymized.</p> <p>There are 6 different datasets:</p> <div> <h3><strong>data_users.csv</strong></h3> </div> <p>This data contains the users. Each user has had one or more purchase events with conversations and/or web sessions prior to that purchase. The data contains 5 columns:</p> <ul> <li>user_id. The ID of a user.</li> <li>purchase_event_id. The ID of a purchase event.</li> <li>conversation_id. The ID of a conversation.</li> <li>session_id. The ID of a web session.</li> <li>event_number. A number specifying the order of conversations/web sessions.</li> </ul> <div> <h3><strong>data_conversations_keyword.csv</strong></h3> </div> <p>This data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with keywords. The data contains 4 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>keywords. List with the IDs of the keywords in the sentence.</li> </ul> <div> <h3><strong>data_conversations_embedding.csv</strong></h3> </div> <p>The data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with text embeddings. The data contains 771 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>embedding_1 - embedding_768. Text embeddings computed with a pre-trained language-specific BERT model.</li> </ul> <div> <h3><strong>data_sessions.csv</strong></h3> </div> <p>This data contains the web sessions that the user made prior to the user's purchase event. Each web session consists of multiple actions. The data contains 3 columns:</p> <ul> <li>session_id. The ID of a web session.</li> <li>action_number. A number specifying the order of actions.</li> <li>action_tags. List with the IDs of the section, object and type of an action.</li> </ul> <div> <h3><strong>data_purchase_events.csv</strong></h3> </div> <p>This data contains the purchase events. Each event consists of one or more item purchases made by the same user. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul> <div> <h3><strong>data_post_filter.csv</strong></h3> </div> <p>This data contains the items that were possible for the user to buy at the time of the user's purchase event. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul>
IntelliMan_WP4_Adaptive Shared Autonomy_T4.2_Advanced human-robot interaction modalities_human robot handover_v0
<p><span>The dataset contains data related to the experiments presented in the publication:</span></p> <p><em><span>M. Costanzo, C. Natale and M. Selvaggio, "Visual and Haptic Cues for Human-Robot Handover*," 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 2677-2682, doi: 10.1109/RO-MAN57019.2023.10309480.</span></em></p>
The Pontifical Bolivarian University and 2030 Agenda: Scientific production, access modalities, and social media impact.
<p><span>The 2030 Agenda and SDGs promote global resilience. Universities play a crucial role in their implementation through research and education. Recent studies analyze SDG-related scientific production, but evaluating the accessibility and social impacts of these results remains lacking. The article aims to analyze the intersection between SDGs, open access, and social impact of science, based on an analysis of PBU academic production in Colombia. A mixed-method approach is employed, combining quantitative and qualitative elements with scientometric techniques and data mining analysis, as well as big data from the perspective of Social Sciences. PBU scientific production analysis reveals increased research activity, with most publications available in open access. Thematic focuses such as health, sustainable development, and energy are highlighted. Thematic diversity reflects a multidisciplinary approach, but there is low production in key areas of sustainable development. Social media attention highlights topics like health, equality, and energy. Open access shows a strong correlation with online attention, emphasizing its importance in amplifying research impact. Although closed research also receives online attention, especially from academics. Greater focus is needed on inclusive publishing strategies and social media dissemination to address global challenges.</span></p>
Modal analysis of thick-walled cylinders
<p><strong>FRF (Frequency Response Function): </strong>This measures how a system responds to dynamic forces at various frequencies. It is used in modal analysis to study vibrations and dynamic behavior of structures. The FRF helps understand how thick-walled cylinders respond to different vibrational modes and frequencies.</p> <div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>A <code>.uff</code> file is a Universal File Format (UFF) used in the field of structural analysis, modal analysis, and vibration testing. To read and process <code>.uff</code> files in Python, you can use the <strong>pyuff</strong> library, which is specifically designed for handling UFF files.</p> </div> </div> </div> </div> </div> </div>
A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment
<p>All processed data used in the manuscript 'A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment'. For more detail please refer to the manuscript. If there is any question please contact Bokai Zhu via email: BZHU0@MGH.HARVARD.EDU.</p> <p>Data folder structure:</p> <p>Main data (eg. presented in main figures etc): Please see .zip file 'data_submission.zip'.</p> <p>Additional data (eg. during revision, or other misc files): Please see .zip file 'data_submission_part2.zip'.</p>
The Icy dataset - a multi-modalities dataset for icy surface reconstruction
<p>Three-dimensional (3D) reconstruction serves as a cornerstone in various robotic applications, playing critical roles in scene understanding and navigation. Traditionally, LiDAR has been instrumental in generating precise point clouds of the environment, providing essential data for these applications. However, the efficacy of LiDAR sensors is significantly hindered in challenging conditions, such as the presence of water or icy surfaces. The complex interplay between laser beams and icy or non-ideal surfaces can result in signal degradation, distortion, or even complete signal loss, adversely affecting the accuracy and reliability of the 3D reconstruction process. The reflective and refractive properties of ice, along with its variable surface conditions, present challenges that traditional LiDAR sensors struggle to address. This paper proposes a diverse dataset to facilitate a multimodal approach for detecting and reconstructing icy surfaces using various sensors. A preliminary study on our dataset demonstrates that, in addition to the geometrical surface obtained by registering consecutive scans from LiDAR, regions with ice can be identified by leveraging visual data to enhance understanding of the surface texture. The integration of distinct data sources can thus improve the robustness of reconstruction algorithms in diverse scenarios. </p>
Establishing a Dual Generational Modality Dataset: Comparing the Ride-Sharing Adoption Trends and Perspectives of Consumers from two Generational Cohorts, Millennials and Gen-Xers - E2
<p>Ride-hailing services such as Uber or Lyft are the latest tool in sustainable transportation strategies to come under scrutiny. Originally thought to be a way to reduce congestion, these services have actually been shown to increase it in some cases. Although the number of individuals driving around urban centers to find parking appears to decrease with the adoption of ride-hailing, Uber or Lyft drivers are instead circling around waiting for riders. Additionally, ride-hailing services have not led to the abandonment of personal vehicles, but rather to the abandonment of public transit in some cases.</p> <p>The purpose of this study is to evaluate the use of ride-hailing services in the two largest age cohorts in the United States: Millennials and Generation X-ers, focusing on the Southeastern states of Florida and North Carolina. This study seeks to determine how each generation has adopted these methods to help planners learn how to incorporate these strategies in transportation planning.</p> <p>The data provides information about the mode of transportation, trip details, and important socio-economic indicators of 1903 respondents from Florida and North Carolina.</p>
Supplementary material S28: Modal response of the petri-dish and accelerometer.
<p>Accelerometer modal response, with petri-dish, petri-dish and lid, and alone. An artificial vibration was driven at a 25% magnitude of the PC maximum output volume with a frequency sweep from 0 to 24kHz, via an electromagnetic shaker, to the accelerometer (green), accelerometer glued to the petri-dish (black) and the accelerometer glued to the petri-dish with the lid placed on (red). These figures demonstrate the linearity of the recording system in both linear and logarithmic scales. A large deviation from linearity is seen at 18kHz, where our signals of interest are seldom seen. The only other discrepancy is seen at 500Hz, where the petri-dish exhibits a resonance. The electromagnetic shaker is incapable of vibrating the accelerometer beyond 21kHz, causing the large drop of response seen at ultra-high frequencies.</p>
A Multivariate Index for Tropical intraseasonal Oscillations based on the Seasonally-varying Modal Structures
<pre>This repository contains the data for "A Multivariate Index for Tropical intraseasonal Oscillations based on the Seasonally-varying Modal Structures". </pre>
TestDataset for Oneat networks for detection and prediction of dividing Hela cells for different imaging modalities
<p>Here we present the tif files to be used to test the Oneat networks for detection and prediction of division events for Hela cells imaged under different imaging modalities and presented originally here: https://zenodo.org/record/6139958#.Yjcjl3rMJD8</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.