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400 results for “immersion”
Figure 4 in Effect of hot-water immersion on eggs and larvae of Anastrepha grandis (Macquart, 1846) (Diptera: Tephritidae) "in vitro and on squash (Cucurbita moschata Duchesne, 1786)
Figure 4. Percent reduction of adult emergence of A. grandis when Atlas squashes infested by larvae was subjected to hydrothermal treatment at different temperatures and times of exposure. / Reducción porcentual de la emergencia de adultos de A. grandis cuando las calabazas Atlas infestadas por larvas se sometieron al tratamiento hidrotermal a diferentes temperaturas y tiempos de exposición.
Figure 2 in Effect of hot-water immersion on eggs and larvae of Anastrepha grandis (Macquart, 1846) (Diptera: Tephritidae) "in vitro and on squash (Cucurbita moschata Duchesne, 1786)
Figure 2. Percent reduction of adult emergence of A. grandis when larvae were subjected to hydrothermal treatment in vitro at different temperatures and exposure times. / Reducción porcentual de la emergencia de adultos de A. grandis cuando las larvas se sometieron al tratamiento hidrotermal in vitro a diferentes temperaturas y tiempos de exposición.
Figure 1 in Effect of hot-water immersion on eggs and larvae of Anastrepha grandis (Macquart, 1846) (Diptera: Tephritidae) "in vitro and on squash (Cucurbita moschata Duchesne, 1786)
Figure 1. Mortality (%) of A. grandis eggs in vitro subjected to hydrothermal treatment at different temperatures and times of exposure. / Mortalidad (%) de huevos de A. grandis in vitro sometidos al tratamiento hidrotermal a diferentes temperaturas y tiempos de exposición.
Figure 3 in Effect of hot-water immersion on eggs and larvae of Anastrepha grandis (Macquart, 1846) (Diptera: Tephritidae) "in vitro and on squash (Cucurbita moschata Duchesne, 1786)
Figure 3. Percent reduction of adult emergence of A. grandis when Atlas squashes infested by eggs was subjected to hydrothermal treatment at different temperatures and times of exposure. / Reducción porcentual de la emergencia de adultos de A. grandis cuando las calabazasAtlas infestadas por huevos fueron se sometieron al tratamiento hidrotermal a diferentes temperaturas y tiempos de exposición.
Partial Saturation Values for Sandstones Immersed in DI Water and Kerosene
<p>This dataset provides the partial saturation values (expressed as a percentage) for sandstone samples immersed in either deionized water or kerosene for 19 hours</p>
Immerse Downstream User Toolbox - example data for FerryBox validation
<p>Example data for FerryBox Validation use case within </p> <p><a href="https://github.com/immerse-project/Downstream-Users-Toolbox">immerse-project/Downstream-Users-Toolbox: Analysis and assessment tools from IMMERSE WP8 (github.com)</a></p> <p>containing exemplary temperature and salinity data from a ship attached FerryBox (<a href="https://www.ferrybox.org/">https://www.ferrybox.org/</a>) and according surface fields of the the CMEMS Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast model <a href="https://resources.marine.copernicus.eu/product-detail/NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013/INFORMATION">NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013</a>, the high resolution NNEMO.v4.2_RC Southern North Sea configuration developed at <a href="https://www.hereon.de/">Helmholtz-Zentrum Hereon</a> in the context of IMMERSE, and the German Bight configuration operated at Hereon, using the <a href="http://ccrm.vims.edu/schismweb/">SCHISM unstructured grid moddeling framework</a> for an assesment of the model performance on those variables within the south eastern German Bight on 2018-11-05.</p>
Immersive haptic simulation for training nurses in emergency medical procedures - Data collected and statistical analysis
<p>Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper.</p> <table> <caption>HR ALL</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.857</td> <td>29</td> <td>0.008</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-8.089</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>7.567</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.962</td> <td>29</td> <td>0.006</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST MANN</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>1.665</td> <td>14</td> <td>0.118</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-7.104</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>6.498</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-1.461</td> <td>14</td> <td>0.166</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST VR</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.341</td> <td>14</td> <td>0.035</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-4.612</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>4.482</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.688</td> <td>14</td> <td>0.018</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR BETWEEN GROUPS</caption> <thead> <tr> <th> </th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-1.958</td> <td>28</td> <td>0.060</td> </tr> <tr> <td>Mann post HR</td> <td>-1.902</td> <td>28</td> <td>0.068</td> </tr> <tr> <td>VR pre HR</td> <td>-4.013</td> <td>28</td> <td>< .001</td> </tr> <tr> <td>VR post HR</td> <td>-2.344</td> <td>28</td> <td>0.026</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Independent samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the mannequin.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>128.333</td> <td>10.715</td> <td>SBP pre-simulator</td> <td>134.533</td> <td>11.819</td> <td>-1.870</td> <td>14</td> <td>0.083</td> </tr> <tr> <td>SBP post-mannequin</td> <td>125.600</td> <td>11.648</td> <td>SBP post-simulator</td> <td>131.467</td> <td>14.643</td> <td>-2.094</td> <td>14</td> <td>0.055</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>80.133</td> <td>5.527</td> <td>DBP pre-simulator</td> <td>81.533</td> <td>9.039</td> <td>-0.623</td> <td>14</td> <td>0.544</td> </tr> <tr> <td>DBP post-mannequin</td> <td>78.667</td> <td>6.956</td> <td>DBP post-simulator</td> <td>81.400</td> <td>8.475</td> <td>-2.073</td> <td>14</td> <td>0.057</td> </tr> <tr> <td>HR pre-mannequin</td> <td>92.133</td> <td>14.837</td> <td>HR pre-simulator</td> <td>75.733</td> <td>9.9625</td> <td>6.498</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>87.400</td> <td>9.132</td> <td>HR post-simulator</td> <td>91.400</td> <td>14.217</td> <td>-1.461</td> <td>29</td> <td>0.166</td> </tr> </tbody> </table> <p>SBP = Systolic blood pressure. DBP = Diastolic blood pressure. HR = Heart Rate.</p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the ParaVR simulator.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>119.067</td> <td>12.898</td> <td>SBP pre-simulator</td> <td>130.600</td> <td>12.188</td> <td>-3.799</td> <td>14</td> <td>0.002</td> </tr> <tr> <td>SBP post-mannequin</td> <td>117.533</td> <td>13.410</td> <td>SBP post-simulator</td> <td>128.200</td> <td>13.385</td> <td>-4.022</td> <td>14</td> <td>0.001</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>76.533</td> <td>8.943</td> <td>DBP pre-simulator</td> <td>80.200</td> <td>6.899</td> <td>-1.815</td> <td>14</td> <td>0.091</td> </tr> <tr> <td>DBP post-mannequin</td> <td>74.333</td> <td>8.541</td> <td>DBP post-simulator</td> <td>79.133</td> <td>7.864</td> <td>-2.003</td> <td>14</td> <td>0.065</td> </tr> <tr> <td>HR pre-mannequin</td> <td>102.067</td> <td>12.876</td> <td>HR pre-simulator</td> <td>91.533</td> <td>11.825</td> <td>4.482</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>95.867</td> <td>14.623</td> <td>HR post-simulator</td> <td>103.667</td> <td>14.450</td> <td>-2.688</td> <td>29</td> <td>0.018</td> </tr> </tbody> </table>
9+10+8 Immersive Music Production Audio and Documentation Archive.GEIDAI.WH
<p>This repository contains several resources related to research on the effect of floor-level loudspeakers on 3D audio reproduction, undertaken by Will Howie, Toru Kamekawa, Miki Morinaga, and Atsushi Marui at Tokyo University of the Arts, November 2021 - November 2023. Please follow the guidelines of usage found in the READ ME file. "Audio" contains 29ch (9+10+8) interleaved audio files of short excerpts of five immersive recordings of musical sound scenes. "Documentation" contains text-based, diagrammatical, and photographic documentation of the recording sessions that yielded these audio excerpts.</p>
Context-dependent memory recall in HMD-based immersive virtual environments
<p>Data represent result of an original VR-based experimental research, which studied human memory recollection in different visual environments. 92 students of psychology using head-mounted VR displays were introduced into a computer-generated virtual environment and asked to memorize presented lists of words. Afterwards, the number of accurate and false memory recollections were measured when participants were placed in the same, respectively different virtual environment. </p> <p> </p>
Effects of immersive virtual reality with treadmill in subjects with Rett Syndrome: a pilot study
<p>This dataset is related to a group of 9 girls affected by Rett Syndrome that performed three/four sessions of virtual reality based activity by means of the GRAIL system (Motek).</p><p>Participants underwent an initial assessment of the cognitive and motor aspects using the Modified Raven matrix, the Rett Assessment Rating Scale (RARS) and the Rett Syndrome Gross Motor Scale (GMS). These baseline assessments were performed by an experienced therapist prior to the access to the GRAIL.</p><p>The project consisted of 4 sessions of GRAIL activities in 1 week. During the first 3 sessions, exergames and augmented feedback activities were proposed. During the fourth session a gait analysis was conducted for the subjects with a suitable motor and behavioral level .</p><p>During all the sessions, a video recording was conducted to detect facial expression of the subjects, in order to use this data to compute the "happiness index". The exergames were administered until the therapists detected any sign of discomfort by the subject.</p><p>The outcome of the training, reported in the database were:</p><ul><li>happiness index</li><li>endurance time</li><li>performance speed</li><li>attention focus on scenario</li><li>suitability evaluation questionnaire</li><li>multistep gait analysis with the GRAIL (only 4 patients).</li></ul><p>The Happiness Index and the Suitability Evaluation Questionnaire investigated the feasibility and tolerability of the training with positive results. Additionally, an increase in walking speed, endurance time and focus attention was detected. When possible, subjects underwent a gait assessment whose results provide insights, although preliminary, concerning differences in gait pattern amongst the recruited subjects. Despite the small sample size, the dataset suggests new strategies for Rett rehabilitation.</p><p> </p>
Determination of mean effective shore level to delineate periods of emersion and immersion for intertidal rocky shores
<p>This dataset contains data described in the paper entitled "Environmental filtering causes increasingly negative effects towards the range limit of an invasive mussel". Data include: the R script to estimate temperature thresholds and calculate effective shore levels (<strong>esl_function.R</strong>); data files associated with this R script (<strong>material.RData</strong>); mean effective shore levels determined for the five South African rocky-shore sites where submersible temperature data loggers were installed (<strong>esl_overall_results.csv</strong>); measurements of mussel shell lengths, level of endolithic infestation, and counts of epibionts on the basibiont mussel <em>Mytilus galloprovincialis</em> (<strong>mussel_data.csv</strong>); counts of barnacles (<em>Chthamalus dentatus</em>) and mussels (<em>Mytilus galloprovincialis</em> and <em>Perna perna</em>) on emersed rocks using 50 × 50 cm quadrats and counts of mussels (<em>Mytilus galloprovincialis</em> and <em>Perna perna</em>) in mono-layer mixed-species mussel patches using 25 × 25 cm quadrats (<strong>quadrat_data.csv</strong>); hourly temperatures recorded by submersible temperature data loggers (<em>EnvLogger</em> Version 2.4; precision of ≤ 0.1 ºC and accuracy of ≤ 0.2 ºC) from September 2019 to September 2020 at the five South African rocky-shore sites (<strong>temperature_data.csv</strong>); and 2019 and 2020 hourly tidal predictions (courtesy of the South African Navy Hydrographic Office) for the closest maritime port to the sites where the temperature data loggers were installed (<strong>tide_data.csv</strong>).</p>
Immersive Analytics: The Influence of Flow, Sense of Agency, and Presence on Performance and Satisfaction
<p>This are the datasets and R scripts for the experiments described in the paper "Immersive Analytics: The Influence of Flow, Sense of Agency, and Presence on Performance and Satisfaction".</p>
Data for: Geolocation and immersion loggers reveal year-round residency and consequent nutrient deposition rates of adult red-footed boobies in the Chagos Archipelago, tropical Indian Ocean
<p>Bio-logging has revealed much about high-latitude seabird migratory strategies, but migratory behaviour in tropical species may differ, with implications for understanding nutrient deposition. Here we use combined light-level and saltwater immersion loggers to study the year-round movement behaviour of adult red-footed boobies (<em>Sula sula rubripes</em>) from the Chagos Archipelago, tropical Indian Ocean to assess migratory movements and estimate nutrient deposition rates based on the number of days they spent ashore. Light levels suggest that red-footed boobies are resident in the Chagos Archipelago year-round, although there are large latitudinal errors this close to the equator. Immersion data also indicate residency with tracked birds returning to land every one or two days. Spending an average of 79.86 ± 2.80 days and 280.84 ± 2.64 nights per year on land allows us to estimate that the 21,670 pairs of red-footed boobies deposit 37.34 ± 0.56 tonnes year<sup>-1</sup> of guano-derived nitrogen throughout the archipelago. Our findings have implications for tropical seabird conservation and phylogenetics, as well as for assessing the impact of seabird nutrients on coral reef ecosystems.</p>
Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past
<p><strong>IMPORTANT NOTE</strong></p> <p>The song <em>B-A-B-Y</em> was written by Isaac Hayes and David Porter, then also arranged by Booker T. Jones and Steve Cropper, and performed by Carla Thomas in 1966.</p> <p>The following binaural sound remixes of <em>B-A-B-Y</em> have been performed in a research context, with four various HRTF profiles coming from the Listen HRTF Database by Ircam, for the AES research paper “Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past”, presented at the 156th AES Convention on June 15-17, 2024, in Madrid, Spain. </p> <p>I, the undersigned Jean Viardot, the first author of this paper, testify that nothing about the composition, the arrangement, or any element of the musical content of the song, has been manipulated to obtain this remix. Only the sound rendering of a digitalized version of the original master has been manipulated and modified to address the research questions being stated in the abovementioned paper.</p> <p>To date (5/11/2024), I have not made any profit with this remix, and I forbid anyone to publish it without my permission. For any intention to publish, broadcast, or market this remix, please contact me beforehand.</p>
Towards Immersive Process Simulation for Declarative Models - Accompanying Material
<div>This dataset contains the research protocol, transcripts and codings for the paper “Towards Immersive Process Simulation for Declarative Models.” Accepted for publication at the International Conference on Business Process Management - Forum. 2024.</div> <div> </div> <div>The documents are ordered by number. We describe their contents below:</div> <div> </div> <div>1. Appendix A - Consent Form used in research and validation interviews in section 4 and section 6</div> <div>2. Appendix B - DCR Training Material used to familiarize the research participants with DCR in section 4</div> <div>3. Appendix C - Elicitation Interview Script & Validation Session Interview Protocol used in sections 4 and 6</div> <div>4. Appendix D - First round of coding of Elicitation interviews</div> <div>5. Appendix E - Second round of coding of Elicitation interviews</div> <div>6. Appendix F - Third round of coding of Elicitation interviews</div> <div>7. Appendix G - First round of coding of Validation interviews</div> <div>8. Appendix H - Second round of coding of Validation interviews</div> <div>9. Appendix I1 - Third round of coding of Validation interviews, part 1</div> <div>10. Appendix I2 - Third round of coding of Validation interviews, part 2</div> <div>11. Appendix J - Validation participant DCR graph representation preferences: questionnaire and answers</div> <p>Moreover, we included all the transcripts of the interviews in raw form. </p> <div>The file transcript_ideation.pdf contains the transcripts of the requirement elicitation interviews conducted during the research phase.</div> <div>The file transcript_validation.pdf contains the the transcripts of the validation interviews.</div> <p> </p> <p>The github repository https://github.com/GloriousHypnotoad/DCR-Graph-artifact contains the source code of the software artifact used in the paper.</p>
Dataset for "Using Adaptive Immersive Environments to Stimulate Emotional Expression and Connection in Dementia Care: Insights from User Perspectives towards SENSE-GARDEN"
<p>This dataset contains all qualitative interview data recorded from early stage research on an adaptive, immersive, multi-sensory intervention that is being developed for people living with dementia (SENSE-GARDEN). 52 semi-structured interviews were conducted with people living with mild cognitive impairment, informal caregivers, and professional caregivers across Belgium, Norway, Portugal, and Romania. The aim of these interviews was to collect initial user responses towards SENSE-GARDEN. </p> <p>The pdf file "Registration Sheet and Interview Questions" lists the questions that were asked during the interviews. The excel file "Interview Data with Thematic Analysis" contains all raw interview data with ideas, notes, and codes made during thematic analysis. The first three sheets in the file correspond to the user type (Person with mild cognitive impairment/Informal Caregiver/Professional Caregiver). The fourth sheet, "Overall themes", gives an overview of each theme, subtheme, and relevant quotes belonging to these themes. </p> <p>This research was conducted as part of a larger project. The SENSE-GARDEN project (AAL/Call2016/054-b/2017, implementation period June 2017 - May 2020) is funded by AAL Programme, co-funded by the European Commission and National Funding Authorities of Norway, Belgium, Romania, and Portugal. </p>
Test Database for the Assessment of Immersive Audio Systems
<p>This repository contains a new library of listening material, for the testing of immersive audio systems, that includes synthetic sound sources, speech recordings and short musical and instrumental performances.</p> <p>Evaluation of perceived audio quality is an essential part of spatial audio system design, where listening tests help to reveal any<br> spatial and timbral distortions that occur. Selection of audio stimuli constitutes an important part of listening test methods, as different stimuli will reveal specific properties of the perceived audio. A wide range of listening test material is therefore required, from which the most appropriate stimuli can be chosen based on the context of the test. For researchers in the field of immersive audio, availability of such materials can be sparse due to the differing requirements of surround sound and ambisonic testing. To this end a new test database has been developed, for use in the spatial and timbral evaluation of immersive audio systems.</p> <p>---</p> <p>The data is organised as follows:</p> <p><strong>Source Files</strong><br> - Source_2-Pop (1kHz tone, one frame long (25ms or 50ms))<br> - Source_3rdOctaveBandPinkNoise (10 & 60 second durations, frequency bands; 32, 64, 125, 250, 500, 1k, 2k, 4k, 8k, 16kHz)<br> - Source_500-2000Hz_PinkNoise (Pink noise with frequencies below 500Hz removed & cut-off at 2kHz)<br> - Source_AcousticGuitar&Vocals (4 original pieces consisting of multiple guitar, vocal, drum & shaker tracks)<br> - Source_ConversationalSpeech (selection of short conversations & passages recorded in an anecohic chamber and reverberant classroom)<br> - Source_DTMF_Tones (Tone pairs consisting of lower & higher frequencies with durations of 1s, 10s, 100ms & 200ms)<br> - Source_GreenwichTimeSignal (series of five 0.1 second, 1 kHz tone bursts separated by 0.9 seconds of silence concluded by a 0.5 second 1 kHz tone)<br> - Source_PinkNoise (durations of 1s, 10s, 60s, 100ms & 200ms)<br> - Source_SinePureTones (1s, 10s, 60s, 100ms & 200ms durations, frequencies; 20, 32, 64, 125, 250, 440, 500, 1k, 2k, 4k, 8k, 16k, 20kHz)<br> - Source_SpeechMaterial(Female) (includes sentences & passages; speaker positions & names; azimuth & elevation angles (-180 to +180); Numbers, alphabet & assorted audio terms)<br> - Source_SpeechMaterial(Male) (includes sentences & passages; speaker positions & names; azimuth & elevation angles (-180 to +180); Numbers, alphabet & assorted audio terms)<br> - Source_SpeechMaterial(Mandarin) (includes only sentences & passages)<br> - Source_WhiteNoise (durations of 1s, 10s, 60s, 100ms & 200ms)</p> <p><strong>Ambisonically Encoded Files</strong><br> - XOrder_3rdOctPinkNoise (X, <strong>where X = 1st, 3rd, 5th, 7th</strong>, Order encoded 1/3 Octave Band Pink noise files) <br> - Cube (sources encoded to cube face positions)<br> - 3rdOctPinkNoise_32Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 32Hz center frequency)<br> - 3rdOctPinkNoise_64Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 64Hz center frequency)<br> - 3rdOctPinkNoise_125Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 125Hz center frequency)<br> - 3rdOctPinkNoise_250Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 250Hz center frequency)<br> - 3rdOctPinkNoise_500Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 500Hz center frequency)<br> - 3rdOctPinkNoise_1000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 1kHz center frequency)<br> - 3rdOctPinkNoise_2000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 2kHz center frequency)<br> - 3rdOctPinkNoise_4000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 4kHz center frequency)<br> - 3rdOctPinkNoise_8000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 8kHz center frequency)<br> - 3rdOctPinkNoise_16000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 16kHz center frequency)<br> - Dodecahedron (sources encoded to dodecahedron face positions)<br> - 3rdOctPinkNoise_32Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 32Hz center frequency)<br> - 3rdOctPinkNoise_64Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 64Hz center frequency)<br> - 3rdOctPinkNoise_125Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 125Hz center frequency)<br> - 3rdOctPinkNoise_250Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 250Hz center frequency)<br> - 3rdOctPinkNoise_500Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 500Hz center frequency)<br> - 3rdOctPinkNoise_1000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 1kHz center frequency)<br> - 3rdOctPinkNoise_2000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 2kHz center frequency)<br> - 3rdOctPinkNoise_4000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 4kHz center frequency)<br> - 3rdOctPinkNoise_8000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 8kHz center frequency)<br> - 3rdOctPinkNoise_16000Hz_-20dBFS_10s_48kHz_24Bit (10 seconds, 16kHz center frequency)<br> - XOrder_500-2000Hz_PinkNoise (X Order encoded 500-2000Hz Pink noise files)<br> - Cube (sources encoded to cube face positions)<br> - 500-2000Hz_PinkNoise_-20dBFS_1s_48kHz_24Bit (1 second)<br> - 500-2000Hz_PinkNoise_-20dBFS_10s_48kHz_24Bit (10 seconds)<br> - 500-2000Hz_PinkNoise_-20dBFS_60s_48kHz_24Bit (60 seconds)<br> - 500-2000Hz_PinkNoise_-20dBFS_100ms_48kHz_24Bit (100 milliseconds)<br> - 500-2000Hz_PinkNoise_-20dBFS_200ms_48kHz_24Bit (200 milliseconds)<br> - Dodecahedron (sources encoded to dodecahedron face positions)<br> - 500-2000Hz_PinkNoise_-20dBFS_1s_48kHz_24Bit (1 second)<br> - 500-2000Hz_PinkNoise_-20dBFS_10s_48kHz_24Bit (10 seconds)<br> - 500-2000Hz_PinkNoise_-20dBFS_60s_48kHz_24Bit (60 seconds)<br> - 500-2000Hz_PinkNoise_-20dBFS_100ms_48kHz_24Bit (100 milliseconds)<br> - 500-2000Hz_PinkNoise_-20dBFS_200ms_48kHz_24Bit (200 milliseconds)<br> - XOrder_BroadcastSources (X Order encoded 2-pip, GTS & DTMF tone files)<br> - 2-Pop_-20dBFS_25ms_48kHz_24Bit (encoded to cube face positions)<br> - 2-Pop_-20dBFS_50ms_48kHz_24Bit (encoded to cube face positions)<br> - DTMF_Tones_-20dBFS_1s_48kHz_24Bit (1 second, encoded to front center position)<br> - DTMF_Tones_-20dBFS_10s_48kHz_24Bit (10 seconds, encoded to front center position)<br> - DTMF_Tones_-20dBFS_100ms_48kHz_24Bit (100 milliseconds, encoded to front center position)<br> - DTMF_Tones_-20dBFS_200ms_48kHz_24Bit (200 milliseconds, encoded to front center position)<br> - GTS_Full_-20dBFS_48kHz_24Bit (encoded to cube face positions)<br> - XOrder_ExampleTestFiles (X Order encoded Pink noise announced example test files e.g. "Front Center" *Noise burst at front center*)<br> - 1Second (announced 1 second pink noise encoded to ITU-R BS.2159-4 and SMPTE 2603 speaker positions)<br> - 100ms_3Bursts (announced 3 bursts of 100ms pink noise encoded to ITU-R BS.2159-4 and SMPTE 2603 speaker positions)<br> - 200ms_3Bursts (announced 3 bursts of 200ms pink noise encoded to ITU-R BS.2159-4 and SMPTE 2603 speaker positions)<br> - XOrder_MovingSources (X Order encoded noise sources that circle azimuth/elevation at specified speeds)<br> - PinkNoise_-20dBFS_60s_48kHz_24Bit (60 seconds, azimuth & elevation pink noise at 45, 90 & 180 degrees per second)<br> - WhiteNoise_-20dBFS_60s_48kHz_24Bit (60 seconds, azimuth & elevation white noise at 45, 90 & 180 degrees per second)<br> - XOrder_PinkNoise (X Order encoded Pink noise files)<br> - Cube (sources encoded to cube face positions)<br> - PinkNoise_-20dBFS_1s_48kHz_24Bit (1 second)<br> - PinkNoise_-20dBFS_10s_48kHz_24Bit (10 seconds)<br> - PinkNoise_-20dBFS_60s_48kHz_24Bit (60 seconds)<br> - PinkNoise_-20dBFS_100ms_48kHz_24Bit (100 milliseconds)<br> - PinkNoise_-20dBFS_200ms_48kHz_24Bit (200 milliseconds)<br> - Dodecahedron (sources encoded to dodecahedron face positions)<br> - PinkNoise_-20dBFS_1s_48kHz_24Bit (1 second)<br> - PinkNoise_-20dBFS_10s_48kHz_24Bit (10 seconds)<br> - PinkNoise_-20dBFS_60s_48kHz_24Bit (60 seconds)<br> - PinkNoise_-20dBFS_100ms_48kHz_24Bit (100 milliseconds)<br> - PinkNoise_-20dBFS_200ms_48kHz_24Bit (200 milliseconds)<br> - XOrder_WhiteNoise (X Order encoded White noise files)<br> - Cube (sources encoded to cube face positions)<br> - WhiteNoise_-20dBFS_1s_48kHz_24Bit (1 second)<br> - WhiteNoise_-20dBFS_10s_48kHz_24Bit (10 seconds)<br> - WhiteNoise_-20dBFS_60s_48kHz_24Bit (60 seconds)<br> - WhiteNoise_-20dBFS_100ms_48kHz_24Bit (100 milliseconds)<br> - WhiteNoise_-20dBFS_200ms_48kHz_24Bit (200 milliseconds)<br> - Dodecahedron (sources encoded to dodecahedron face positions)<br> - WhiteNoise_-20dBFS_1s_48kHz_24Bit (1 second)<br> - WhiteNoise_-20dBFS_10s_48kHz_24Bit (10 seconds)<br> - WhiteNoise_-20dBFS_60s_48kHz_24Bit (60 seconds)<br> - WhiteNoise_-20dBFS_100ms_48kHz_24Bit (100 milliseconds)<br> - WhiteNoise_-20dBFS_200ms_48kHz_24Bit (200 milliseconds)</p> <p>---</p> <p>For any enquiries regarding the data please email: ho581@york.ac.uk</p> <p>Data produced by Harry Ogden at the Audio Lab, Department of Electronics Engineering, University of York<br> Contact: ho581@york.ac.uk</p> <p>Funding was provided by UK Engineering and Physical Sciences Research Council (EPSRC), the Department of Electronic Engineering at the University of York.</p>
The Intersection of Tibetan Buddhism & Western Psychology: A Cultural Immersion Study
<p>Data Set used for qualitative research project re: Intersection of Buddhism and Western Psychology.<br> <br> Abstract: Western psychology has a long history of incorporating Buddhist practices and traditions into aspects of treatment, perhaps most predominantly the practice and theory of mindfulness (Bankart, 2003; Chan-Wiles, 2013; Dryden & Still, 2006; Kress, 1993; Rhys-Davids, 1924). This qualitative study critically examines the integration of Buddhism and psychology through the lived experiences of four clinical psychology doctoral students. These students attended a 9-day meditation retreat and seminar in France which involved the study of Tibetan Buddhism and how it integrates with Western psychology. The lived experience was captured through in vivo journaling, retrospective journaling, and interviews between the students. This material was then analyzed using the Consensual Qualitative Research (CQR) procedure outlined by Hill (2012), which incorporates phenomenological grounded theory and comprehensive process analysis (Hill et al., 2005). Four themes were identified: acceptance as an aspect of creating change, greater awareness for the concepts of Buddhism, Bodhicitta and the value of suffering, and integrating the experience and carrying it forward. The content of these themes is explored, along with implications for increased cultural sensitivity and clinical effectiveness in the implementation of mindfulness practices and other Tibetan Buddhist concepts in the practice of Western psychology.</p>
Resources for BMF CP 84: Gender, age, income, immersiveness, and space tourism intention
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>How are gender, age, and income associated with the intention to try space tourism?</span></li> <li><span>How are gender, age, and income associated with the intention to try space tourism when conditional on the level of immersiveness in information on social media?</span></li> </ul>
Resources for BMF CP 82: Space tourism, information trustworthiness, usefulness, and immersiveness
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>How are information trustworthiness, usefulness, and immersiveness associated with the general intention to try space tourism?</span></li> <li><span>Does the uncertainty of information (proxied by information trustworthiness) moderate the associations between information usefulness, immersiveness, and the general intention to try space tourism?</span></li> </ul>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.