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584 results for “elicitation”
SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Skin Vibrations Across the Upper Limb
<p>The repository contains the data for the toolbox released as part of the publication “SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb.” The toolbox and installation and usage instructions can be found on GitHub here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>. If you use these data or our toolbox please cite our publication: <a href="https://doi.org/10.1109/HAPTICS59260.2024.10520852">https://doi.org/10.1109/HAPTICS59260.2024.10520852</a>.</p> <p>Full citation: “Tummala, N., Reardon, G., Fani, S., Goetz, D., Bianchi, M., and Visell, Y. (2024) SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb. IEEE Haptics Symposium 2024. DOI: 10.1109/HAPTICS59260.2024.10520852” </p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.</p> <p><strong> </strong></p> <p><strong>Dataset Description</strong></p> <p>This dataset comprises experimental data of 3-axis surface acceleration at 72 locations on the skin in response to unit impulsive forces supplied at 20 different input locations on the palmar hand surface. For details on our experimental procedure, please see our publication. This data is intended to be used as part of the SkinSource toolbox, which can be found here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>.</p> <p><strong> </strong></p> <p><strong>Data Fields</strong></p> <p>The data is provided as a .mat file. This file contains a single variable “dataTable” of variable type “table.” The table contains 80 rows, each corresponding to a unique experimental condition (4 participants x 20 input locations), and contains the following fields:</p> <p><strong>Data </strong>(522x72x3) - 3D array containing the 3-axis skin acceleration at 522 time points (impulse responses) for each of 72 accelerometers. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for the accelerometer locations on the dorsal surface of the upper limb.</p> <p><strong>Model </strong>- The upper limb model number. This number specifies the participant that data was taken on.</p> <p><strong>Location</strong> -<strong> </strong>Number designating which input location on the palmar hand surface the data corresponds to. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for input location number mapping.</p>
Duhumbi Elicitation - Sound files
<p>This upload contains all the .wav sound files of the written elicitation notes belonging to the 'Grammar of Duhumbi (Chugpa)' (Brill, 2019). These notes are accompanied by the original transcripts of the notes in pdf format (DOI 10.5281/zenodo.1406850). Please note that the elicitation sessions are mainly in Tshangla and Duhumbi. Also, the analysis from the notes, including the labels and glosses, the description, and the phonetic representation, may differ from the final analysis in the grammatical description. For any questions please contact the author directly.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
SSVEP database elicited by four visual stimuli types
<ul> <li>The database consists of 108 electroencephalographic files from 27 participants performing a 5-target selection task. </li> <li>Each participant performed one experimental session.</li> <li>All datasets were collected on channels PO7, PO3, POz, PO4, PO8, O1, Oz, and O2, according to the 10–20 EEG electrode placement standard.</li> <li>For the visual stimuli, we consider the On-Off and Checkerboard patterns with luminance modulated by rectangular and sinusoidal functions, resulting in a total of four types of visual stimuli: Checkerboard pattern with the rectangular modulated signal (Sxx-C.txt); Checkerboard pattern with sinusoidal modulated signal (Sxx-mC.txt); On-Off pattern with sinusoidal modulated signal (Sxx-mOO.txt) and On-Off pattern with rectangular modulated signal (Sxx-OO.txt), where "Sxx" represents the subject number and 01 <= xx <= 27.</li> <li>In each file columns 1 to 8 correspond to EEG data and column 9 corresponds to the marks channel.</li> <li>Each phase of the experiment block is identified with a marker.</li> <li>The phases of one experiment trial are Fixation(201), Target Presentation(202), Preparation(203), Stimulation(101-105), and Rest(200).</li> <li>Marker numbers 101, 102, 103, 104, and 105, encodes de target frequency applied during the "Stimulation" stage in a trial. They are associated with the stimulation frequencies as follows: 101 - 24 Hz; 102 - 20 Hz; 103 - 15 Hz; 104 - 10.909 Hz and, 105 - 8.57 Hz</li> <li>Files can be easily accessible with EEG-dedicated MATLAB toolboxes, such as Fieldtrip and EEGLAB.</li> </ul>
Are you scared yet? Variations to cue components elicits differential prey behavioral responses even when gape limited predators are relatively small.
Anti-predator behavior is often evoked based on measurements of risk calculated from sensory cues emanating from predators independent of physical attack. Yet, the exact sensory indices of cues used in risk assessment remain largely unknown. To examine how different predatory cue indices of information are used in risk assessment, we presented prey with various cues from sublethal gape-limited predators. Rusty crayfish (Faxonius rusticus (Girard, 1852)) were exposed to predatory odors from sublethal-sized largemouth bass (Micropterus salmoides (Lacepède, 1802)) to test effects of changing predator abundance, relative size relationships, and total predator length in flow through mesocosms. Foraging, shelter use, and movement behavior were used to measure cue effects. Foraging time depended jointly upon predator abundance and total predator size (p = 0.030). Specifically, high predator abundance resulted in decreased foraging efforts as gape ratio increased. Similarly, sheltering time depended on the interaction between predator abundance and gape ratio when predator abundance was highest (p = 0.020). Crayfish significantly increased exploration time when gape ratio increased (p = 0.010). Thus, this study shows crayfish can use different indices of predatory cues, namely total predator abundance and relative size ratios, in risk assessment but do so in context-specific ways.
Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
<p>Emitting conspicuous signals into the environment to attract mates comes with the increased risk of interception by eavesdropping enemies. As a defence, a commonly described strategy is for signallers to group together in leks, diluting each individual's risk. Lekking systems are often highly social settings in which competing males dynamically alter their signalling behaviour to attract mates. Thus, signalling at the lek requires navigating fluctuations in risk, competition, and reproductive opportunities. Here, we investigate how behavioural defence strategies directed at an eavesdropping enemy have cascading effects across the communication network. We investigated these behaviours in the túngara frog (<em>Engystomops pustulosus</em>), examining how a calling male's swatting defence directed at frog-biting midges indirectly affects the calling behaviour of his rival. We found that the rival responds to swat-induced water ripples by increasing his call rate and complexity. Then, performing phonotaxis experiments, we found that eavesdropping fringe-lipped bats (<em>Trachops cirrhosus</em>) do not exhibit a preference for a swatting male compared to his rival, but females strongly prefer the rival male. Defences to minimize attacks from eavesdroppers thus shift the mate competition landscape in favour of rival males. By modulating the attractiveness of signalling prey to female receivers, we posit that eavesdropping micropredators likely have an unappreciated impact on the ecology and evolution of sexual communication systems.</p>
Kara Nonopai elicitation with storyboard
<p>In this sound file, also with ELAN transcription, speakers of Kara Nonopai describe pictures from a storyboard I amde called "John na Lassey" (John and Lassey). The storyboard is included in the archived data.</p>
Kara Nonopai elicitation with pictures from stimuli kits
<p>In this record, Speakers of Kara Nonopai comment on the pictures from the following stimuli kits: </p> <p><span><span>-<span> </span></span></span><span>Skopeteas, S., Fiedler, I., Hellmuth, S., Schwarz, A., Stoel, R., Fanselow, G., Féry, C., Krifka, M. (2006). Questionnaire on Information Structure: Reference Manual. Interdisciplinary studies on information structure, Vol. 4. Universitätsverlag Potsdam. Retrieved on </span><span><a href="http://www.sfb632.uni-potsdam.de/downloads/quis/ref_manual.pdf"><span>http://www.sfb632.uni-potsdam.de/downloads/quis/ref_manual.pdf</span></a></span><span> </span></p> <p><span><span>-<span> </span></span></span><span>Bowerman, M., Gullberg, M., Majid, A. & Narasimhan, B. 2004. Put project: The crosslinguisticencoding of placement events. In Field Manual, Vol. 9, A. Majid (ed.), 10–18. Nijmegen:Max Planck Institute for Psycholinguistics. <br><em>(13) (PDF) Put project: The cross-linguistic encoding of placement events</em>. Available from: </span><span><a href="https://www.researchgate.net/publication/257022334_Put_project_The_cross-linguistic_encoding_of_placement_events"><span>https://www.researchgate.net/publication/257022334_Put_project_The_cross-linguistic_encoding_of_placement_events</span></a></span><span>. Material available at </span><span><a href="http://fieldmanuals.mpi.nl/regulations-on-use/"><span>http://fieldmanuals.mpi.nl/regulations-on-use/</span></a></span><span> </span></p> <p><span><span>-<span> </span></span></span><span>Cut and break clips from </span><span><a href="http://fieldmanuals.mpi.nl/volumes/2001/cut-and-break-clips/"><span>http://fieldmanuals.mpi.nl/volumes/2001/cut-and-break-clips/</span></a></span><span>. Bohnemeyer, J., Bowerman, M., & Brown, P. (2001). Cut and break clips. In S. C. Levinson, & N. J. Enfield (Eds.), <em>Manual for the field season 2001</em> (pp. 90-96). </span><span>Nijmegen: Max Planck Institute for Psycholinguistics. doi:<a href="https://doi.org/10.17617/2.874626">10.17617/2.874626</a>. </span></p>
Experiment package for Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment
<p>Full experimental materials, scripts, and results for "Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment"</p>
Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation - Supplementary Material
<p>This is the supplementary material for the paper "Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation". </p> <p>Abstract: App store-inspired elicitation is the practice of exploring competitors’ apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages and possible issues. This paper aims to study strategies, benefits and challenges of app store-inspired elicitation, and compare this technique with more traditional requirements elicitation interviews. We conduct an experimental simulation with 58 analysts, and collect qualitative data. Our results show that specific guidelines and procedures are required to better conduct app store-inspired elicitation. Furthermore, current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and<br> evaluation of competing products. While interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges, and suggests possible benefits to foster app store-inspired elicitation among practitioners.</p> <p>The package contains the following files:</p> <p>1.Protocol.pdf - it describes in details the steps of the protocol and the intermediate results obtained during the execution.</p> <p>2. Codebooks:<br> 2.a. Codebook Strategies: codebook of the strategies to select apps<br> 2.b Codebook Benefits: codebook of the benefits of use IBE (sheet 1) and ASE (sheet 2)<br> 2.c Codebook Challenges: codebook of the challenged of use IBE (sheet 1) and ASE (sheet 2)<br> 2.d Differences IBE-ASE: table of the identified (categorized) differences between IBE and ASE</p> <p>3. Labelled Data <br> 3.a Strategies - labelled data: the file contains the name of the selected apps, the motivation behind the selection, and the themes assigned to them (refer to 2.a for explanation of the themes).<br> 3.b Benefits IBE - labelled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.b for explanation of the themes).<br> 3.c Challenges IBE - labelled data: the file contains the extract of the raw data about IBE challenges and the themes assigned (refer to 2.c for explanation of the themes).<br> 3.d Benefits ASE - labelled data: the file contains the extract of the raw data about ASE benefits and the themes assigned (refer to 2.b for explanation of the themes).<br> 3.e Challenges ASE - labelled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.c for explanation of the themes).</p> <p>4. Raw data.xls: it contains the raw data used in the work (two sheets, one for strategies and one for reflections).</p> <p>5. SLR data: data related to the lightweight systematic literature review <br> 5.a Codebook Scopus.xlsx: codebook for the themes elicited from the SLR. The themes are also present in the files in the folder Codebooks.<br> 5.b SLR-scopus-results-and-selected.xlsx: results of the search string, and, in green, the selected papers. </p> <p>6. Readme.txt: summary file. </p> <p>Note that some of the row in Raw data.xls (and in the corresponding "Labelled Data" files) are substituted with N/A. This corresponds to those participants who asked to not publicly share their responses.</p>
Data to accompany "Automatic text clustering for audio attribute elicitation experiment responses", AES 143rd Convention, New York, NY, USA, 2017
<p>This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1) and the BBC as part of the BBC Audio Research Partnership. Details about the data underlying this work, along with the terms for data access, are available from http://dx.doi.org/10.15126/surreydata.00841589.</p> <p>If you use the data, please cite the following paper:</p> <p>J. Francombe, T. Brookes, and R. Mason, “Automatic text clustering for audio attribute elicitation experiment responses”, AES 143rd Convention, New York, NY, USA, 2017</p>
Duhumbi Elicitation - Notes
<p>This upload contains all the pdfs of the written elicitation notes belonging to the 'Grammar of Duhumbi' (Brill, 2019). These notes are accompanied by the original sound files (DOI 10.5281/zenodo.1406852). Please note that the elicitation sessions are mainly in Tshangla and Duhumbi. Also, the analysis from the notes, including the labels and glosses, the description, and the phonetic representation, may differ from the final analysis in the grammatical description. For any questions please contact the author directly.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Data for: Network of autoscopic hallucinations elicited by intracerebral stimulations of periventricular nodular heterotopia: an SEEG study
<p>Periventricular nodular heterotopias (PVNH) are areas of neurons abnormally located in the white matter that might be involved in physiological cortical functions. Autoscopic hallucinations are changes in self-consciousness determined by a mismatch in integration of multiple sensory inputs. Our goal is to highlight the brain network involved in generation of autoscopic hallucination elicited by electrical stimulation of a PVNH in a drug resistant epilepsy patient.</p> <p> Our patient was explored using stereo-electroencephalography with electrodes covering the right posterior temporal PVNH and the adjacent cortex. Direct electrical high frequency stimulation of the PVNH elicited autoscopic hallucinations mainly involving the face and upper trunk. We then used multiple modalities to determine brain connectivity: single pulse electrical stimulation of the PVNH and stimulation-evoked potentials were used to highlight resting state effective connectivity. High-frequency stimulation using alternating polarity pulses enabled us to identify the network involved, time-locked to the clinical effect and to map symptom-related effective connectivity. Functional connectivity using a non-linear regression method was used to determine dependencies between different cortical regions following the stimulation. Finally, structural connectivity was highlighted using deterministic fiber tracking.</p> <p>Multi-modal connectivity analysis identified a network involving the PVNH, occipital and temporal neocortex, fusiform gyrus and parietal cortex.</p>
Requirements elicitation (ReqElic) in my company
<p>Questionnaire (online survey) about requirements elicitation (ReqElic) in my company, PDF generated from <https://docs.google.com/forms/d/1RH_oMgpreDCvHexh4dKe40EVhsoBXPaXbbOYMITQDlQ/edit></p>
DATASET Skin-Based Vaccination: A Systematic Mapping Review of the Types of Vaccines and Methods Used and Immunity and Protection Elicited in Pigs
<p>Dataset listing all publications used in systematic mapping review. This file contains the dataset, a dictionary and values worksheets for the following publication: </p> <p>Skin-Based Vaccination: A Systematic Mapping Review of the Types of Vaccines and Methods Used and Immunity and Protection Elicited in Pigs.</p> <p>Vaccines 2023</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/?sort=date&size=200&term=C%C3%B3-Rives+I&cauthor_id=36851328">Inés Có-Rives</a> <a href="https://pubmed.ncbi.nlm.nih.gov/36851328/#full-view-affiliation-1">1</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/?sort=date&size=200&term=Chen+AY&cauthor_id=36851328">Ann Ying-An Chen</a> <a href="https://pubmed.ncbi.nlm.nih.gov/36851328/#full-view-affiliation-1">1</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/?sort=date&size=200&term=Moore+AC&cauthor_id=36851328">Anne C Moore</a> <a href="https://pubmed.ncbi.nlm.nih.gov/36851328/#full-view-affiliation-1">1</a></p> <ul> <li>PMID: 36851328</li> <li>PMCID: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/pmc9962282/">PMC9962282</a></li> <li>DOI: <a href="https://doi.org/10.3390/vaccines11020450">10.3390/vaccines11020450</a></li> </ul> <p>The advantages of skin-based vaccination include induction of strong immunity, dose-sparing, and ease of administration. Several technologies for skin-based immunisation in humans are being developed to maximise these key advantages. This route is more conventionally used in veterinary medicine. Skin-based vaccination of pigs is of high relevance due to their anatomical, physiological, and immunological similarities to humans, as well as being a source of zoonotic diseases and their livestock value. We conducted a systematic mapping review, focusing on vaccine-induced immunity and safety after the skin immunisation of pigs. Veterinary vaccines, specifically anti-viral vaccines, predominated in the literature. The safe and potent skin administration to pigs of adjuvanted vaccines, particularly emulsions, are frequently documented. Multiple methods of skin immunisation exist; however, there is a lack of consistent terminology and accurate descriptions of the route and device. Antibody responses, compared to other immune correlates, are most frequently reported. There is a lack of research on the underlying mechanisms of action and breadth of responses. Nevertheless, encouraging results, both in safety and immunogenicity, were observed after skin vaccination that were often comparable to or superior the intramuscular route. Further research in this area will underlie the development of enhanced skin vaccine strategies for pigs, other animals and humans.</p> <p><strong>Keywords: </strong>epicutaneous; epidermal; intradermal; needle-free; percutaneous; pig; skin; transcutaneous; transdermal; vaccine.</p>
Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings
<p><strong>Masked Emotion FilmClip Dataset (MEFD): Emotion Elicitation with Facial Coverings</strong></p><p> </p><p>The Masked Emotion FilmClip Dataset (MEFD) stands as an avant-garde assembly of emotion-inducing video clips tailored for a unique niche - the elicitation of emotions in individuals wearing facial masks. This dataset emerges in response to the global need to understand emotional cues and expressions in the backdrop of widespread facial mask usage. Assembled by leveraging the synergies between cinematography and psychological research, MEFD serves as an invaluable trove for researchers, especially those in AI, seeking to decode emotions even when a significant portion of the face is concealed.</p><p><strong>Dataset Highlights</strong>:</p><ul><li><strong>Facial Masks</strong>: All subjects in the video clips are seen wearing facial masks, replicating real-world scenarios and augmenting the dataset's relevance.</li><li><strong>Film Titles</strong>: The title of each selected film enriching the context of the emotional narrative.</li><li><strong>Emotion Label</strong>: Clear emotion classification associated with every clip, ensuring replicability in emotional elicitation.</li><li><strong>Clip Duration</strong>: Precise duration details ensuring standardized exposure and consistent emotion elicitation.</li><li><strong>Curated with Expertise</strong>: Clips have undergone rigorous evaluation by seasoned psychologists and film critics, affirming their effectiveness in eliciting the designated emotion.</li><li><strong>Consent and Ethics</strong>: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.</li></ul><p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p><p>Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395</p><p>A paramount feature of MEFD is its emphasis on "key moments". These timestamps, a product of collective expertise from psychologists and film critics, guide the researcher to intervals of heightened emotional resonance within the clips, especially challenging to discern with masked faces.</p><p><strong>Key Emotional Moments within Dataset</strong>:</p><p>Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334</p><p> </p><p>-----------------</p><p>DATA STRUCTURE<br>-----------------</p><p>SADNESS_XXX.CSV<br>timestamp emotion<br>1625062890.938222 NEUTRAL --> Initial time start for the neutral video<br>1625062932.567609 SADNESS --> Initial time start for the EMOTION video</p><p><br>Notes:<br>** Subject id 15: FEAR label started to fast; Neutral data very few<br>-----------------</p><p> </p><p><i>The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.</i></p><p>MEFD is more than just a dataset; it is a testament to human resilience and adaptability. As facial masks become ubiquitous, understanding the nuances of masked emotional expressions becomes imperative. MEFD rises to this challenge, bridging gaps and pioneering a new frontier in emotion research.</p>
Gilman-Adhered FilmClip Emotion Dataset (GAFED): Tailored Clips for Emotional Elicitation
<p><strong>Gilman-Adhered FilmClip Emotion Dataset (GAFED): Tailored Clips for Emotional Elicitation</strong></p> <p><strong>Description</strong>:</p> <p>Introducing the Gilman-Adhered FilmClip Emotion Dataset (GAFED) - a cutting-edge compilation of video clips curated explicitly based on the guidelines set by Gilman et al. (2017). This dataset is meticulously structured, leveraging both the realms of film and psychological research. The objective is clear: to induce specific emotional responses with utmost precision and reproducibility. Perfectly tuned for researchers, therapists, and educators, GAFED facilitates an in-depth exploration into the human emotional spectrum using the medium of film.</p> <p><strong>Dataset Highlights</strong>:</p> <ul> <li><strong>Gilman's Guidelines</strong>: GAFED's foundation is built upon the rigorous criteria and insights provided by Gilman et al., ensuring methodological accuracy and relevance in emotional elicitation.</li> <li><strong>Film Titles</strong>: Each selected film's title provides an immersive backdrop to the emotions sought to be evoked.</li> <li><strong>Emotion Label</strong>: A focused emotional response is designated for each clip, reinforcing the consistency in elicitation.</li> <li><strong>Clip Duration</strong>: Standardized duration of every clip ensures a uniform exposure, leading to consistent response measurements.</li> <li><strong>Curated with Precision</strong>: Every film clip in GAFED has been reviewed and handpicked, echoing Gilman et al.'s principles, thereby cementing their efficacy in triggering the intended emotion.</li> </ul> <p><strong>Emotion-Eliciting Video Clips within Dataset</strong>:</p> Film Targeted Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395 <p>The crowning feature of GAFED is its identification of "key moments". These crucial timestamps serve as a bridge between cinema and emotion, guiding researchers to intervals teeming with emotional potency.</p> <p><strong>Key Emotional Moments within Dataset</strong>:</p> Film Targeted Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334 <p><strong>Based on</strong>: Gilman, T. L., et al. (2017). A film set for the elicitation of emotion in research. Behavior Research Methods, 49(6).</p> <p>GAFED isn't merely a dataset; it's an amalgamation of cinema and psychology, encapsulating the vastness of human emotion. Tailored to perfection and adhering to Gilman et al.'s insights, it stands as a beacon for researchers exploring the depths of human emotion through film.</p>
Simultaneous modulation of pulse charge and burst period elicits two differentiable referred sensations
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Data for: Network of autoscopic hallucinations elicited by intracerebral stimulations of periventricular nodular heterotopia: an SEEG study
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Data from: Variation in sexual signals and defensive strategies elicits receiver-dependent shifts in attractiveness
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Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
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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.