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388 results for “Intentions”
Engagement and Purchase Intention in storydoing and storytelling for Instagram ads
<p>This is a database with which we have worked on the engagement and purchase intention generated by storydoing and storytelling advertising.</p>
DataSet-Neural signatures of linguistic predictions and listener's attention to speaker's communication intention
<p>Researchers can find the raw EEG data with scripts of EEG data analyses, false alarms data, and sentence materials related to he project entitled "Neural signatures of linguistic predictions and listener's attention to speaker's communication intention". Corrections due to the available article form: "I-" was replaced with "E-" and "I+" was replaced with "E+" in the online article form. In the same manner, "INTENTION" should be replaced with "PROSODIC EMPHASIS" in the scripts.</p>
Consumer's purchase intention and willingness to pay (WTP) for circular beef
<p>Data correspond to 5246 consumers responsible for buying food in their homes in 5 EU countries (Germany, Netherlands, Italy, Czech Republic, and Spain). The collected information corresponds to 1) socio-demographic information, 2) t consumer’s purchase intention and willingness to pay for beef obtained by circular farming (DCE), 3) and variables related to the components of the planned behaviour theory (social norms, environmental attitudes, consumer´ perceived behavioural control and frequency of consumption, and general sustainable behaviour).</p> <p>Data was collected online using a structured survey </p> <p>Data allow us to know consumers' preferences and WTP for beef obtained by circular farming and some sustainable behaviours</p>
ITALIC: An Italian Intent Classification Dataset
<p><strong>ITALIC: An Italian Intent Classification Dataset</strong></p> <p>ITALIC is a dataset of Italian audio recordings and contains annotation for utterance transcripts and associated intents. The ITALIC dataset was created through a custom web platform, utilizing both native and non-native Italian speakers as participants. The participants were required to record themselves while reading a randomly sampled short text from the MASSIVE dataset.</p> <p>ITALIC dataset containing 16,521 audio recordings collected by 70 different volunteers. The dataset is composed of:</p> <ul> <li><em>recordings</em>: a folder containing the audio recordings in <em>.wav</em> format. It contains all the recordings composing the data collection.</li> <li><em>[CONFIG_NAME]_[SPLIT_NAME].json</em>: the files containing metadata used for generating the configuration proposed in the paper and their corresponding splits: <ul> <li><em>[CONFIG_NAME]</em> is the name of the configuration, e.g. <em>massive</em>, <em>hard_noisy</em>, or <em>hard_speaker</em>. For the description of the configurations, please refer to the paper.</li> <li><em>[SPLIT_NAME]</em> is the name of the split, e.g. <em>train</em>, <em>validation</em>, or <em>test</em>. Each split is different for each configuration.</li> </ul> </li> </ul> <p> </p> <p>The metadata files are in JSON format, with one sample per line. Each sample is a JSON object with the following fields:</p> <ul> <li><em>id</em>: the unique identifier of the sample.</li> <li><em>age</em>: the age of the speaker (self-reported)</li> <li><em>gender</em>: the gender of the speaker (self-reported)</li> <li><em>region</em>: the region of origin of the speaker (self-reported)</li> <li><em>nationality</em>: the nationality of the speaker (self-reported)</li> <li><em>lisp</em>: the presence of a lisp in the speaker (self-reported)</li> <li><em>education</em>: the education level of the speaker (self-reported)</li> <li><em>speaker_id</em>: the unique identifier of the speaker</li> <li><em>environment</em>: the environment in which the recording was made (self-reported)</li> <li><em>device</em>: the device used for recording (self-reported)</li> <li><em>scenario</em>, <em>field</em>, <em>intent</em>: the information parsed from <a href="https://github.com/alexa/massive">massive</a> annotations and accompanying metadata.</li> <li><em>utt</em>: the utterance to be spoken by the speaker. This information is also taken from <a href="https://github.com/alexa/massive">massive</a>.</li> </ul> <p> </p> <p><strong>Important Note:</strong></p> <p><strong>By downloading and accessing the dataset, you agree not to attempt to determine the identity of speakers in the ITALIC dataset or to clone their voices.</strong></p> <p> </p> <p><strong>License</strong></p> <p>The ITALIC dataset is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p>If you use the dataset in your work, please cite the ITALIC paper.</p>
Dataset of Intention to Whistleblow: Perception of Reporting Skill Mediates the Predicting Role of Class Consciousness and Perceived Probability of Revenge
<p><em>Dataset of </em><strong>Intention to Whistleblow: Perception of Reporting Skill Mediates the Predicting Role of Class Consciousness and Perceived Probability of Revenge</strong></p>
Exploring Changes in COVID-19 Vaccination Intentions by Prompting Altruistic Motives Using a Video Intervention
ClinicalTrials.gov study NCT04960228. IPD Sharing: NO. Countries: 1. Publications: 3.
Evaluating the usefulness of Protection Motivation Theory for predicting climate change mitigation behavioral intentions among a US sample of climate change deniers and acknowledgers
Open the record for dataset details and reuse information.
A neural signature of contextually mediated intentional forgetting
Open the record for dataset details and reuse information.
Knocking Sound Effects With Emotional Intentions
<p>The dataset was recorded by the professional foley artist Ulf Olausson at the FoleyWorks (http://foleyworks.se/) studios in Stockholm on the 15th October, 2019. Inspired by previous work on knocking sounds [1]. we chose five type of emotions to be portrayed in the dataset: anger, fear, happiness, neutral and sadness.</p> <p>In order to imagine a situation where the knocking action is performed with a particular emotional intention, we provided the foley artist with common situations where these actions can happen. The emotions, alongside the context are the following:</p> <ul> <li><strong>Anger</strong>: telling a flatmate for the 4th time to turn down the very loud music.</li> <li><strong>Fear</strong>: alerting a neighbor of a possible risk.</li> <li><strong>Happiness</strong>: telling a flatmate you won a prize.</li> <li><strong>Neutral</strong>: parcel delivery.</li> <li><strong>Sadness</strong>: telling a friend someone passed away.</li> </ul> <p>We also encouraged the foley artist to perform diverse interpretations of the context provided in order to have a wider variety of sounds. The dataset was recorded with a Rode NT1 microphone, performing the knocks to a closed door.</p> <p>We recorded a total of 600 knocking actions (120 actions per category). An action is a sequence of individual knocks. We discarded 20 actions per category to filter out unwanted noise. The final 500 audio files were edited only to trim the audio so each file starts on the first knock onset and finish on the last knock decay. </p> <p> </p> <p> </p> <p>[1] R. Vitale and R. Bresin, “Emotional Cues in Knocking Sounds,” in 10th International Conference on Music Perception and Cognition, Sapporo, Japan, August 25-29, 2008, 2008, p. 276.</p>
Data from: Are signals of aggressive intent less honest in urban habitats?
How anthropogenic change affects animal social behavior, including communication is an important question. Urban noise often drives shifts in acoustic properties of signals but the consequences of noise for the honesty of signals – i.e. how well they predict signaler behavior – is unclear. Here we examine whether honesty of aggressive signaling is compromised in male urban song sparrows (Melospiza melodia). Song sparrows have two honest close-range signals: the low amplitude soft songs (an acoustic signal) and wing waves (a visual signal) but whether the honesty of these signals is affected by urbanization has not been examined. If soft songs are less effective in urban noise, we predict that they should predict attacks less reliably in urban habitats compared to rural habitats. We confirmed earlier findings that urban birds were more aggressive than rural birds and found that acoustic noise was higher in urban habitats. Urban birds still sang more soft songs than rural birds. High rates of soft songs and low rates of loud songs predicted attacks in both habitats. Thus, while urbanization has a significant effect on aggressive behaviors, it might have a limited effect on the overall honesty of aggressive signals in song sparrows. We also found evidence for a multimodal shift: urban birds tended to give proportionally more wing waves than soft songs than rural birds, although whether that shift is due to noise-dependent plasticity is unclear. These findings encourage further experimental study of the specific variables that are responsible for behavioral change due to urbanization.
Predicting the pre-service teachers' teaching intention from educator-created (dis)empowering climates: A self-determination theory-based longitudinal approach
<p><span>Guided by self-determination theory, this two-wave longitudinal research aims to examine the associations between pre-service teachers’ perceptions of educator-created (dis-)empowering climates and their teaching intention, considering the bright and dark motivational pathways. A total of 1,258 secondary pre-service teachers (55.5% women, <em>M<sub>age =</sub></em> 26.17, <em>SD </em>= 5.66) have participated. The results from path analysis have shown positive associations between educator-created empowering climates and need satisfaction, autonomous motivation, and teaching intention in pre-service teachers, while educator-created disempowering climates have been positively related to pre-service teachers’ need frustration, controlled motivation and amotivation. The conclusions have suggested that pre-service teachers’ perceptions of educator-created (dis-)empowering climates during their initial teacher education programme play a determining role in their teaching intention. </span></p>
Artifact for ACM CSUR article: "A Meta-Study of Software-Change Intentions"
<p>This archive includes the CSV files with the raw and aggregated data we used for our meta study:</p> <p>"A Meta-Study of Software-Change Intentions" published at the ACM Computing Surveys journal.</p> <p> </p> <p>Please refer to the readme for details on the files.</p>
Intentional Observer Effects on Quantum Randomness
<p>The research that created this data is described in:</p> <ul> <li>Maier, M. A., Dechamps, M. C., & Pflitsch, M. (2018). Intentional Observer Effects on Quantum Randomness: A Bayesian Analysis Reveals Evidence Against Micro-Psychokinesis. Frontiers in psychology, 9, 379. doi:10.3389/fpsyg.2018.00379</li> </ul> <p>Two files are included:</p> <ul> <li>exp_data_12571.csv - Raw data (experiments)</li> <li>sim_data_12571.csv - Raw data (simulation)</li> </ul>
Intention d'abandonner ses études chez les étudiants et Covid-19 : une application de la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (nombre d’observations = 415, période : mars 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable finale que le modèle cherche à expliquer (la variable dépendante) est l’intention d’abandonner ses études chez les étudiants. Elle inclut la mesure des conditions de vie durant la pandémie du Covid-19 (17 items).</p>
Survey of risk perception, trust, and behavioral intention during the COVID-19 pandemic
<p>Early public health strategies to prevent the spread of COVID-19 in the United States relied on non-pharmaceutical interventions (NPIs) as vaccines and therapeutic treatments were not yet available. Implementation of NPIs, primarily social distancing and mask wearing, varied widely between communities within the US due to variable government mandates, as well as differences in attitudes and opinions. To understand the interplay of trust, risk perception, behavioral intention, and disease burden, we developed a survey instrument to study attitudes concerning COVID-19 and pandemic behavioral change in three states: Idaho, Texas, and Vermont. We designed our survey (<em>n </em>= 1034) to detect whether these relationships were significantly different in rural populations. The best fitting structural equation models show that trust indirectly affects protective pandemic behaviors via health and economic risk perception. We explore two different variations of this social cognitive model: the first assumes behavioral intention affects future disease burden while the second assumes that observed disease burden affects behavioral intention. In our models we include several exogenous variables to control for demographic and geographic effects. Notably, political ideology is the only exogenous variable which significantly affects all aspects of the social cognitive model (trust, risk perception, and behavioral intention). While there is a direct negative effect associated with rurality on disease burden, likely due to the protective effect of low population density in the early pandemic waves, we found a marginally significant, positive, indirect effect of rurality on disease burden via decreased trust (<em>p</em> = 0.095). This trust deficit creates additional vulnerabilities to COVID-19 in rural communities which also have reduced healthcare capacity. Increasing trust by methods such as in-group messaging could potentially remove some of the disparities inferred by our models and increase NPI effectiveness.</p>
Complementary dataset of Overton metadata on citing policy-related documents for the study "From intent to impact: Investigating the effects of open sharing commitments"
<p>This document provides the underlying dataset for the bibliometric component for the 2022 study "From intent to impact: Investigating the effects of open sharing commitments" by Research Consulting and Science-Metrix.</p> <p>Before reproducing the study findings or re-using the underlying datasets for other purposes, please cautiously review their limitations in the study's technical annex and main report, available at: https://zenodo.org/communities/data-sharing-in-public-health-emergencies/ </p> <p>Special thanks from the Science-Metrix / Elsevier teams to Euan Adie and Overton for this exceptional public release of Overton metadata, and for conducting extraordinary data collection to retrieve citations towards arXiv preprints.</p> <p> </p> <p>Scope: note that this file combines cited journal publications and preprints from the Covid19, HVRD, Zika and HVVD thematic sets.</p> <p>Data treatment: this data is intend foremost to provide manual validation or qualitative triangulation of our findings. No special efforts have been made to process and clean the data for its eventual re-use in secondary analysis or text mining approaches.</p> <p>Definitions used in this table:</p> <table> <tbody> <tr> <td>Column name </td> <td>Definition</td> </tr> <tr> <td>document_type</td> <td>preprint or journal publication</td> </tr> <tr> <td>doi</td> <td>digital object identifier</td> </tr> <tr> <td>arxiv_id</td> <td>arXiv preprint server's unique identifier for its preprints</td> </tr> <tr> <td>ssrn_id</td> <td>SSRN preprint server's unique identifier for its preprints. Note that some of these IDs are contained within the DOIs also assigned to some (but not all) SSRN preprints , in the form of "10.2139/ssrn." + 'ssrn_id'</td> </tr> <tr> <td>coalesce_id</td> <td>coalesce function applied to the DOI, arxiv_id and ssrn_id. Redundant for journal publications.</td> </tr> <tr> <td>policy_source_title</td> <td>name of the policy-related organization</td> </tr> <tr> <td>published_on</td> <td>publication date of the citing policy-related document</td> </tr> <tr> <td>title</td> <td>title of the citing policy-related document</td> </tr> <tr> <td>pdf_url</td> <td>URL for the online version of the policy-related document</td> </tr> <tr> <td>snippet</td> <td>Where available, excerpt of the text immediatly before and after the citation to a journal publication or preprint found in the citing policy-related document</td> </tr> </tbody> </table> <p> </p>
A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy (dataset)
<p>Data cleaned and used for the path analysis model estimated in the article</p> <h1>A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy</h1> <p>https://doi.org/10.3390/su13041879</p>
Resources for BMF CP 83: Information seeking, recommendation mechanism, and space tourism intention
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>What information sources on social media are associated with the general intention to try space tourism?</span></li> <li><span>Does the automatically recommended information moderate the associations between multiple sources of information and the intention to try space tourism?</span></li> </ul>
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>
Assessing consumers' attitudes, expectations and intentions towards health and sustainability regarding seafood consumption in Italy
<p>The EU and its Member States have articulated a sustainability vision ‘to live well within the limits of our planet’ by 2050. In this context, consumers play a key role, being able to drive seafood production sustainability and responsibility according to their behaviour, also in relation to their attitudes towards health, nutrition and wellbeing. On the basis of these premises, this research explores Italian consumers' attitudes towards health and sustainability in relation to seafood, in order to segment different target of consumers. The framework used in this study is mainly focused on a quantitative exploratory data collection based on an online survey. Three groups of consumerswere identified based on general health interest, perceived benefits of eating seafoods and attitude towards seafoods: Health seekerswho eat seafood for duty; Health seekers and seafood lovers; Lowcommitment to health and indifferent to seafood. Differences among groups related to socio-demographic characteristics, sustainability attitudes, intentions and interest in information about seafood productswere also investigated. In particular, the first two groups are more familiar with sustainable seafood products and more interested in information on these products than the third, both in terms of product origin and seasonality. Consumers belonging to second group show a higher probability to buy seafood products considering this characteristic than the other two groups. Based on the results obtained, a strategic plan could be developed to achieve relevant goals in education, communication and sustainability labelling related with seafood products. Following a preliminary scouting carried out with all the seafood stakeholders, a specific territory to test this approach has been already identified in Torre del Cerrano (Italy). These results have a strong implication for policy makers and educational<br> institutions as they identify differences in attitudes and perceptions among consumers that are crucial in order to<br> design the right communication strategy strategies as well as messages content.</p>
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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)
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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.