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59 results for “EMBODIMENT”
Working time, energy throughput and value added embodied in production, consumption and trade by subsectors for the US, the EU, China and rest of the world (2011)
<p>This repository contains the data needed to reproduce the results in:</p> <p>Pérez-Sánchez, L., Velasco-Fernández, R., Giampietro, M., The international division of labor and embodied working time in trade for the US, the EU and China, Ecological Economics. <a href="http://doi.org/10.1016/j.ecolecon.2020.106909">https://doi.org/10.1016/j.ecolecon.2020.1069097</a></p> <p>Sources of data are specified in the dataset (under tab "references")</p> <p> </p>
Supplementary Material for Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics
<p>This dataset accompanies the article "Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics" (Lahnakoski & Bennett et al., submitted). </p> <p>It includes the Neo-Assyrian text corpus that is the basis for the word embeddings, a list of the Akkadian emotion and body words of interest for this study, and the scripts, toolboxes, and data used to generate the heat maps of the body.</p> <p>There is an additional folder containing the high resolution figures included in the article.</p> <p>A detailed ReadMe (README.txt) provides an overview of the folders.</p>
Investigating the Effects of Embodiment on Emotional Categorization of Faces and Words in Children and Adults
<p>The three data files uploaded here contain the data used for the analyses in experiments 1a, 1b, and 2 as described in the article carrying the same title as this dataset, published in the journal Frontiers in Psychology. All analyses were carried out in SPSS version 22 as described in the published article.</p> <p>Article Abstract:</p> <p>The facial feedback hypothesis (FFH) indicates that besides being involved in the production of facial expressions, the musculature of the face also influences one’s perception of emotional stimuli. Recently, this effect has been the focus of increased scrutiny as efforts to replicate a key study with adult participants supporting this hypothesis, using the so-called “pen-in-the-mouth” task, have not been successful at several labs. Our series of experiments attempted to investigate whether the assumed embodiment effect can be reproduced in a simplified emotional categorization task for emotional faces and words. We also wanted to test whether the embodiment effect can be detected in children because it is assumed that their bodily processes are especially closely linked with their sensory and cognitive processes. Our experiments involved child and adult participants categorizing faces and words as positive or negative as quickly as possible, while inducing a positive or negative facial or bodily state (holding a straw in the mouth such that a smile or a frown was generated, or creating a positive or negative body posture). The positive or negative facial and bodily states could therefore be either congruent or incongruent with the valence of the target face and word stimuli. Our results did not show any significant differences between the congruent and incongruent conditions in either children or adults. This suggests that embodiment effects either do not significantly impact valence-based categorization or are not strong enough to be detected by our approach considering the sample size in the present study.</p>
Embodied Spatial Navigation Training in Mild Cognitive Impairment: A Proof-of-Concept Trial
<p>Raw data of included cognitive test and VR data Starting Grant Ricerca Finalizzata, code: SG-2018-12368175</p>
Motion Capture Data for Hand Motion Embodiment
<h1>Dataset</h1> <p>A dataset of human manipulation actions recorded with a motion capture system.</p> <p>A Qualisys motion capture system was used to record the data. We tracked individual finger movements as well as the position and orientation of the right hand. Some recordings contain additional markers at the back, shoulder, and elbow. The motion capture setup is explained <a href="https://dfki-ric.github.io/hand_embodiment/motion_capture_setup.html">here</a>.</p> <p>The dataset contains the original recordings of manipulation actions as well as metadata with annotations of relevant parts of the recordings (labels, start, end). Recordings are exported from the Qualisys Track Manager (QTM) as tab-separated value (TSV) files. Metadata is provided in JSON format. Related software is available at <a href="https://github.com/dfki-ric/hand_embodiment">github.com/dfki-ric/hand_embodiment</a>, which also contains code to load and use the dataset.</p> <h1>Publication</h1> <p>This dataset was introduced in the following paper:</p> <p>Alexander Fabisch, Manuela Uliano, Dennis Marschner, Melvin Laux, Johannes Brust, Marco Controzzi: "A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations", Proceedings of IEEE-RAS International Conference on Humanoid Robots 2022.</p> <p>It is available from <a href="https://arxiv.org/abs/2203.02778">arxiv.org</a> as a preprint or from <a href="https://ieeexplore.ieee.org/document/10000165">IEEE</a>.</p> <p>If you use the dataset, please cite the paper as:</p> <blockquote> <p>@INPROCEEDINGS{Fabisch2022,<br> author={Fabisch, Alexander and Uliano, Manuela and Marschner, Dennis and Laux, Melvin and Brust, Johannes and Controzzi, Marco},<br> booktitle={2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)}, <br> title={A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations}, <br> year={2022},<br> pages={801--808},<br> doi={10.1109/Humanoids53995.2022.10000165}<br>}</p> </blockquote> <h1>Ethics Approval</h1> <p>Experimental protocols were approved by the ethics committee of the University of Bremen. Written informed consent was obtained from all participants for participation in the study and to publish this dataset.</p> <h1>Origin and Funding</h1> <p>This dataset is provided by the Robotics Innovation Center, DFKI GmbH.</p> <p>This work was supported by the European Commission under the Horizon 2020 framework program for Research and Innovation (project acronym: APRIL, project number: 870142).</p>
Historic Embodied Emotions Model (HEEM) dataset
<p>First release of the HEEM dataset.</p>
Source data to create the figures of the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" using REX3 with new GHG extension including LULUCF
<p>This repository contains the source data to create the figures of the study <a href="https://doi.org/10.1038/s43247-025-02144-0">Rising greenhouse gas emissions embodied in the global bioeconomy supply chain</a> published in <em>Communications Earth & Environment</em>. The results were calculated with the REX3 database in Version 3.2 of this repository and the GHG extension and matlab codes in Version 3.4 of this repository.</p> <p>Figure 1, and 3–5 were created in Rstudio with the attached Rcode <em>Bioeconomy_GHG_sankeys.R</em></p> <p>Figure 2 was created in tableau with an <a href="https://public.tableau.com/app/profile/livia.cabernard/vizzes">interactive data visualizer</a> that allows to zoom into the global bioeconomy supply chain.</p>
Measuring embodied conceptualizations of pitch insinging performances: insights from an OpenPose study
<p>People conceptualize auditory pitch as vertical space: low and high pitch correspond to low and high space respectively. The strength of this cross-modal correspondence, however, seems to vary across different cultural contexts and a debate on the different factors underlying this variation is currently taking place. According to one hypothesis, pitch mappings are semantically mediated. For instance, the use of conventional metaphors such as ‘falling’ or ‘rising’ melodies strengthens a pitch-height mapping to the detriment of other possible mappings (e.g. pitch as bright/dark color or small/big size). Hence, entrenched pitch terms shape specific conceptualizations. The deterministic role of language is called into question by the hypothesis that different pitch mappings share a less constraining conceptual basis. As such, conceptual primitives may be concretized <em>ad hoc</em> into specific domains so that more local variation is possible<em>.</em> This claim is supported, for instance, by the finding that musicians use language-congruent (conventional) and language-incongruent (<em>ad hoc</em>) mappings interchangeably. The present paper substantiates this observation by investigating the head movements of musically trained and untrained speakers of Dutch in a melody reproduction task, as embodied instantiations of a vertical conceptualization of pitch. The OpenPose algorithm was used to track the movement trajectories in detail. The results show that untrained participants systematically made language-congruent movements, while trained participants showed more diverse behaviors, including language-incongruent movements. The difference between the two groups could not be attributed to the level of accuracy in the singing performances. In sum, this study argues for a joint consideration of more entrenched (e.g. linguistic metaphors) and more context-dependent (e.g. musical training and task) factors in accounting for variability in pitch representations.</p>
Partially automatically annotated corpus to predict gestural cues in Embodied Conversational Agents
<p>#Structure of the corpus</p> <p>This corpus has been built using speeches of Spanish politicians freely available <a href="http://www.congreso.es/portal/page/portal/Congreso/Congreso/Intervenciones">here</a> along with their transcriptions.</p> <p>Each transcription has been analyzed in terms of:</p> <ul> <li>Surface Syntactic Structure*</li> <li>Deep Syntactic Structure*</li> <li>Morphology (Part of Speech)*</li> <li>Communicative Structure</li> </ul> <p>Gestures (beat vs. no gesture tags) have been annotated using the videos.</p> <p>*All those features have been automatically retrieved using the parser freely available in https://github.com/TalnUPF/miis. The other features have been annotated manually.</p> <p>#Concerns about the corpus</p> <p>This corpus has been mostly annotated manually. Annotation agreement has not been computed.</p> <p>Moreover, it is small. In order to extract reliable correlations from it, it should be extended.</p>
Data for The Design and Formalization of an Embodied Soundscape Sonification Framework
<p>This repository contains evaluation data ane experimental stimuli for the paper :The Design and Formalization of an Embodied Soundscape Sonification Framework.</p>
ERP evidence of embodiment of action-verbs at lexical stages in L1 and L2
<p>EEG data for the article : Britz J., Collaud E., Jost L., Sato S., Bugnon A., Mouthon M. and Annoni JM. ERP evidence of embodiment of action-verbs at lexical stages in L1 and L2. Brain sciences 2024</p> <p>The data used in the study were organized using the Brain Imaging Data Structure (BIDS) (Gorgolewski, K., Auer, T., Calhoun, V. et al., 2016) with the extension for EEG data (Pernet, C.R., Appelhoff, S., Gorgolewski, K.J. et al., 2019).</p> <p> </p> <p>.....</p>
Electrophysiological Signals of Embodiment and MI-BCI Training in VR
<p><strong>DATASET DETAILS:</strong></p> <p><strong>Participant demographics:</strong></p> <p>A total of 26 participants were included, consisting of 10 males (mean age 25.4 ± 7.4) and 16 females (mean age 23 ± 3.2). All participants were right-handed, reported normal or corrected-to-normal vision, and had no motor impairments. Three participants had previous experience with BCIs, and five participants used VR more than twice. Participants were randomly assigned to either the embodied group (N=13) or the non-embodied group (N=13), which served as a control. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki. </p> <p><strong>Experiment Description:</strong></p> <p>A between-subject design was used to investigate the effect of virtual embodiment priming phase on the subsequent motor-imagery training phase in VR. The experiment comprised four main blocks: (1) equipment setup and instructions (45-60 minutes), (2) resting state EEG recording (4 minutes), (3) inducing or breaking the sense of embodiment in VR (5 minutes), and (4) MI training in VR (15 minutes). The entire experiment lasted approximately 90-120 minutes. Directly after block 3, participants answered a questionnaire that measured their subjective sense of embodiment and physical presence.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active <strong>EEG</strong> electrodes (+3 ACC) with a sampling rate of 500Hz. In addition, <strong>EMG, </strong>and <strong>Temperature</strong> signals (in uV) have been recorded synchronously in a bipolar montage and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA).<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>EMG Left</strong> (AUX1): 33<br> <strong>EMG Right</strong>. (AUX2): 34<br> <strong>Temperature</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p> </p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand </td> </tr> <tr> <td>S08</td> <td>class2, Right hand </td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- GROUP [Control or Embodied]<br> | +---USER #<br> | | +---TASK #<br> | | | +---Resting State<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---Embodiment<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p>For demographics and questionnaire data, please contact the authors.</p>
Embodiment of action-related language in the native and a late foreign language – An fMRI-study
<p>Theories of embodied cognition postulate that language processing activates similar sensory-motor structures as<br> when interacting with the environment. Only little is known about the neural substrate of embodiment in a<br> foreign language (L2) as compared to the mother tongue (L1). In this fMRI study, we investigated embodiment of<br> motor and non-motor action verbs in L1 and L2 including 31 late bilinguals. Half had German as L1 and French as<br> L2, and the other half vice-versa. We collapsed across languages to avoid the confound between language and<br> order of language acquisition. Region of interest analyses showed stronger activation in motor regions during L2<br> than during L1 processing, independently of the motor-relatedness of the verbs. Moreover, a stronger involvement<br> of motor regions for motor-related as compared to non-motor-related verbs, similarly for L1 and L2, was<br> found. Overall, the similarity between L1 and L2 embodiment seems to depend on individual and contextual<br> factors.</p>
Data & Figures on the Multiscale Spatiotemporal Characterization of Embodied Environmental Efficiency of Building Structures in Geneva from 1850 to 2018
<p>Process data, figures, and figure data resulting from the study on Multiscale Spatiotemporal Characterization of Embodied Environmental Efficiency of Building Structures in Geneva from 1850 to 2018.</p>
The neural correlates of embodied L2 learning Does embodied L2 verb learning affect representation and retention?
<p>We investigated how naturalistic actions in a highly immersive, multimodal, interactive 3D virtual reality (VR) environment may enhance word encoding by recording EEG in a pre/post-test learning paradigm. While behavior data has shown that coupling word encoding with gestures congruent with word meaning enhances learning, the neural underpinnings of this effect have yet to be elucidated. We coupled EEG recording with VR to examine whether “embodied learning” improves learning and creates linguistic representations that produce greater motor resonance. Participants learned action verbs in an L2 in two different conditions: Specific action (observing and performing congruent actions on virtual objects) and Pointing (observing actions and pointing to virtual objects). Pre and post-training participants performed a Match-mismatch task as we measured EEG (variation in the N400 response as a function of match between observed actions and auditory verbs) and a Passive listening task while we measured motor activation (mu (8-13 Hz) and beta band (13-30Hz) desynchronization during auditory verb processing) during verb processing. Contrary to our expectations, post-training results revealed neither semantic nor motor effects in either group when considered independently of learning success. Behavioral results showed both groups learned the verbs, but also a great deal of variability in learning success. When considering performance, Low performance learners showed no semantic effect and High performance learners exhibited an N400 effect for Mismatch vs Match trails post-training, independent of the type of learning. Taken as a whole, our results suggest that embodied processes can play an important role in L2 learning.</p>
Data from: Harnessing natural embodied intelligence for spontaneous jellyfish cyborgs
Open the record for dataset details and reuse information.
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
The neural correlates of embodied L2 learning. Does embodied L2 verb learning affect representation and retention?
<p>We investigated how naturalistic actions in a highly immersive, multimodal, interactive 3D virtual reality (VR) environment may enhance word encoding by recording EEG in a pre/post-test learning paradigm. While behavior data has shown that coupling word encoding with gestures congruent with word meaning enhances learning, the neural underpinnings of this effect have yet to be elucidated. We coupled EEG recording with VR to examine whether "embodied learning" improves learning and creates linguistic representations that produce greater motor resonance. Participants learned action verbs in an L2 in two different conditions: Specific action (observing and performing congruent actions on virtual objects) and Pointing (observing actions and pointing to virtual objects). Pre and post-training participants performed a Match-mismatch task as we measured EEG (variation in the N400 response as a function of match between observed actions and auditory verbs) and a Passive listening task while we measured motor activation (mu (8-13 Hz) and beta band (13-30Hz) desynchronization during auditory verb processing) during verb processing. Contrary to our expectations, post-training results revealed neither semantic nor motor effects in either group when considered independently of learning success. Behavioral results showed both groups learned the verbs, but also a great deal of variability in learning success. When considering performance, Low performance learners showed no semantic effect and High performance learners exhibited an N400 effect for Mismatch vs Match trails post-training, independent of the type of learning. Taken as a whole, our results suggest that embodied processes can play an important role in L2 learning.</p>
Reaching articular limits can negatively impact embodiment in virtual reality
<p>Dataset for the paper Reaching articular limits can negatively impact embodiment in virtual reality.</p>
Experimental data for the study: "Hiding Assistive Robots During Training in Immersive VR Does not Affect Users' Motivation, Presence, Embodiment, and Performance"
<p>The datasets contains the motor performance metrics, the gaze fixation time ratios, and the questionnaire responses for a study involving a motor task with a rehabilitation assistive robot and an immersive virtual reality head-mounted display. The study was performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact nicolas.wenk@unibe.ch or L.MarchalCrespo@tudelft.nl.</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.