Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,836
datasets available to search
ShareScore release 0.9.0
Dataset results
1,836 results for “emotion”
Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning (Datasets)
<p>This repository contains the datasets used in the article "Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning" (Coutinho & Schuller, 2017). </p> <p>In that article four different data sets were used: SEMAINE, RECOLA, ME14 and MP (acronyms and datasets described below). The SEMAINE (speech) and ME14 (music) corpora were used for the unsupervised training of the Denoising Auto-encoders (domain adaptation stage) - only the audio features extracted from the audio files in these corpora were used and are provided in this repository. The RECOLA (speech) and MP (music) corpora were used for the supervised training phase - both the audio features extracted from the audio files and the Arousal and Valence annotations were used. In this repository, we provide the audio features extracted from the audio files for both corpora, and Arousal and Valence annotations for some of the music datasets (those that the author of this repository is the data curator).</p> <p>Below, you can find description of the various corpora, the details about the data stored in this repository and information on how to obtain the rest of the data used by Coutinho and Schuller (2017).</p> <p><strong>SEMAINE (speech)</strong></p> <p>The SEMAINE corpus (McKeown, Valstar, Cowie, Pantic & Schroder, 2012) was developed specifically to address the task of achieving emotion-rich interactions, and it is adequate for this task as it comprises a wide range of emotional speech. It includes video and speech recordings of spontaneous interactions between human and emotionally stereotyped `characters'. Coutinho & Schuller (2017) used a subset of this database (called <em>Solid-SAL</em>). The <em>Solid-SAL</em> dataset is freely available for scientific research purposes (see http://semaine-db.eu). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/SEMAINE).</p> <p><strong>RECOLA (speech)</strong></p> <p>The RECOLA database (Ringeval, Sonderegger, Sauer & Lalanne, 2013) consists of multimodal recordings (audio, video, and peripheral physiological activity) of spontaneous dyadic interactions between French adults. Coutinho & Schuller (2017) used the RECOLA-Audio module which consists of the audio recordings of each participant in the dyadic phase of the task. In particular, they used the non-segmented high-quality audio signals (WAV format, 44.1kHz, 16bits), obtained through unidirectional headset microphones, of the first five minutes of each interaction. Annotations consist of time-continuous ratings of the level of Arousal and Valence dimensions of emotion perceived by each rater while seeing and listening the audio-visual recordings of each participant task. The publicly available annotated dataset includes only part of the data which amounts to a total number of 23 instances. The time frame length used by Coutinho & Schuller (2017) is 1s (the original annotations were downsampled). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/RECOLA). To obtain the annotations you should contact the author of the original study (see https://diuf.unifr.ch/diva/recola/download.html for further details).</p> <p><strong>ME14 (music)</strong></p> <p>The MediaEval ``Emotion in Music'' task is dedicated to the estimation of Arousal and Valence scores continuously in time and value for song excerpts from the Free Music Archive. Coutinho and Schuller (2017) used the whole corpus (development and test sets for the 2014 challenge) which includes 1,744 songs belonging to 11 musical styles -- Soul, Blues, Electronic, Rock, Classical, Hip-Hop, International, Folk, Jazz, Country, and Pop (maximum of five songs per artist). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/ME14). The full dataset (including annotations) can be obtained from http://www.multimediaeval.org/mediaeval2014/emotion2014/.</p> <p><strong>MP (music)</strong></p> <p>This is a corpus compiled specifically for this work described in Coutinho & Schuller (2017) using data collected in four previous studies. It consists of emotionally diverse full music pieces from a variety of musical styles (Classical and contemporary Western Art, Baroque, Bossa Nova, Rock, Pop, Heavy Metal, and Film Music). Annotations were obtained in controlled laboratory experiments whereby the emotional character of each piece was evaluated time-continuously in terms of levels of Arousal and Valence perceived by listeners (ranging between 35 to 52 in the four studies). In what follows, some details about the various studies are described.</p> <ul> <li>MP<sub>DB1</sub>: This subset of the MP corpus consists of the data reported by Korhonen (2004), and gently made available by the author. This dataset includes six full (or long excerpts) music pieces ranging from 151s to 315s in length (only classical music). Each piece was annotated by 35 participants (14 females). The time series correspondents to each music piece were collected at 1Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features used in Coutinho & Schuller (2017) (under features/MP/DB1). To obtain the labels please contact the author of the original study.</li> <li>MP<sub>DB2</sub>: The dataset by Coutinho & Cangelosi (2011) includes 9 full pieces (43s to 240s long) of classical music (romantic repertoire) annotated by 39 subjects (19 females). Values were recorded every time the mouse was moved with a precision of 1 ms. The resultant timeseries were then resampled (moving average) to a synchronous rate of 1 Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features (under features/MP/DB2) and labels (under annotations/MP/DB2) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB3</sub>: This dataset was collected by Coutinho & Dibben (2012) and it consists of 8 pieces of film music (84s to 130s long) taken from the late 20th century Hollywood film repertoire. Emotion ratings were given by 52 participants (26 females). The annotation procedure, data processing, and golden standard calculations were identical to MP<sub>DB2</sub>. This repository includes the audio features (under features/MP/DB3) and labels (under annotations/MP/DB3) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB4</sub>: This dataset was collected by Grewe, Nagel, Kopiez and Altenmüller (2007), and gently made available by the authors. It includes seven music pieces (127s to 502s in length) of heterogeneous styles (e.g., Rock, Pop, Heavy Metal, Classical). Each music piece was annotated by 38 participants (29 females) using an identical methodology to MP<sub>DB2</sub> and MP<sub>DB3</sub>. Data processing and golden standard calculations were also identical. This repository includes the audio features (under features/MP/DB4) used in Coutinho & Schuller (2017). To obtain the labels contact the authors of the original study</li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Coutinho, E., & Cangelosi, A. (2011). Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. <em>Emotion</em>, <em>11</em>(4), 921.</p> <p>Coutinho, E., & Dibben, N. (2013). Psychoacoustic cues to emotion in speech prosody and music. <em>Cognition & Emotion</em>, <em>27</em>(4), 658-684.</p> <p>Coutinho E, Schuller B (2017) Shared acoustic codes underlie emotional communication in music and speech—Evidence from deep transfer learning. PLoS ONE 12(6): e0179289. https://doi. org/10.1371/journal.pone.0179289.</p> <p>Grewe, O., Nagel, F., Kopiez, R., Altenmüller, E. (2007). Emotions over time: synchronicity and development of subjective, physiological, and facial affective reactions to music. <em>Emotion, 7</em>(4), pp. 774-788. DOI: 10.1037/1528-3542.7.4.774.</p> <p>Korhonen, M. (2004). Modeling Continuous Emotional Appraisals of Music Using System Identification. Available from: http://hdl.handle.net/10012/879.</p> <p>McKeown, G., Valstar, M., Cowie, R., Pantic, M., Schroder, M. (2012). The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. <em>IEEE Transactions on Affective Computing</em>, 3, pp. 5-17. DOI: http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2011.20.</p> <p>Ringeval, F., Sonderegger, A., Sauer, J. & Lalanne, D. (2013). Introducing the RECOLA Multimodal Corpus of Remote Collaborative and Affective Interactions. In <em>Proceedings of the 2nd International Workshop on Emotion Representation, Analysis and Synthesis in Continuous Time and Space (EmoSPACE 2013)</em>, Shanghai, China. IEEE</p>
When fear meets anger: Attitudes toward positively versus negatively evaluated pandemic policy proposals when negative emotions are competing in society
<p>Since the outbreak of the COVID‐19 pandemic, citizens of many countries have been faced with health‐related fear, as well as anti‐establishment and anti‐governmental anger. This emotional landscape colored the ongoing efforts by the authorities to convince citizens to accept various public policy proposals. In two studies (total <em>N</em> = 528, one preregistered) conducted in Poland in two different situations, we focused on the role of the simultaneously evoked pandemic fear and anti‐government anger in shaping attitudes toward the pandemic regulations. For negatively evaluated proposals, both of these emotions worked in opposite directions: fear was associated with increasing support, while anger was associated with increasing rejection. However, for positively evaluated policy proposals, fear and anger worked in consonance, and both were associated with increasing acceptance of the proposed regulations. Thus, while fear seems to motivate the acceptance of even negatively evaluated proposals that are seen as protective ones, anger works to amplify or polarize the proposals’ basic evaluations. Our findings could help plan the implementation of public policies in societies in times of turbulent emotional landscapes.</p>
Abstract Emotives
<p>The exploration of black holes in other universes has long been the focus of<br>scientific fascination. This paper investigates the data-driven, abstract emotive<br>narrative that can be derived from the horizon structuring of such entities. We<br>formulate and solve a mathematical expression to describle the various compo-<br>nents of the system, while also taking into account factors such as anomalies,<br>gravity, and wave-particle interactions. The equation is then solved, leading<br>to a comprehensive set of equations that reveal the unique dynamics of black<br>holes in different universes and its associated abstraction. Finally, we discuss<br>potential implications of our results in the wider context of astrophysics and<br>cosmology.</p>
Emotional Voice Messages (EMOVOME) database
<p>The Emotional Voice Messages (EMOVOME) database is a speech dataset collected for emotion recognition in real-world conditions. It contains 999 spontaneous voice messages from 100 Spanish speakers, collected from real conversations on a messaging app. EMOVOME includes both expert and non-expert emotional annotations, covering valence and arousal dimensions, along with emotion categories for the expert annotations. Detailed participant information is provided, including sociodemographic data and personality trait assessments using the NEO-FFI questionnaire. Moreover, EMOVOME provides audio recordings of participants reading a given text, as well as transcriptions of all 999 voice messages. Additionally, baseline models for valence and arousal recognition are provided, utilizing both speech and audio transcriptions.</p> <h2>Description</h2> <p>For details on the EMOVOME database, please refer to the article:</p> <blockquote> <p><em>"EMOVOME Database: Advancing Emotion Recognition in Speech Beyond Staged Scenarios". Lucía Gómez-Zaragozá, Rocío del Amor, María José Castro-Bleda, Valery Naranjo, Mariano Alcañiz Raya, Javier Marín-Morales. (pre-print available in <a href="https://doi.org/10.48550/arXiv.2403.02167" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2403.02167</a>)<br></em></p> </blockquote> <h2>Content</h2> <p>The Zenodo repository contains four files:</p> <ul> <li><strong>EMOVOME_agreement.pdf</strong>: agreement file required to access the original audio files, detailed in section Usage Notes. </li> <li><strong>labels.csv</strong>: ratings of the three non-experts and the expert annotator, independently and combined.</li> <li><strong>participants_ids.csv</strong>: table mapping each numerical file ID to its corresponding alphanumeric participant ID.</li> <li><strong>transcriptions.csv</strong>:<strong> </strong>transcriptions of each audio.</li> </ul> <p>The repository also includes three folders:</p> <ul> <li><strong>Audios</strong>: it contains the file <strong>features_eGeMAPSv02.csv</strong> corresponding to the standard acoustic feature set used in the baseline model, and two folders: <ul> <li><strong>Lecture</strong>: contains the audio files corresponding to the text readings, with each file named according to the participant's ID.</li> <li><strong>Emotions</strong>: contains the voice recordings from the messaging app provided by the user, named with a file ID.</li> </ul> </li> <li><strong>Questionnaires</strong>: it contains two files: 1) <strong>sociodemographic_spanish.csv</strong> and <strong>sociodemographic_english.csv</strong> are the sociodemographic data of participants in Spanish and English, respectively, including the demographic information; and 2) <strong>NEO-FFI_spanish.csv</strong> includes the participants’ answers to the Spanish version of the NEO-FFI questionnaire. The three files include a column indicating the participant's ID to link the information.</li> <li><strong>Baseline_emotion_recognition</strong>: it includes three files and two folders. The file <strong>partitions.csv</strong> specifies the proposed data partition. Particularly, the dataset is divided into 80% for development and 20% for testing using a speaker-independent approach, i.e., samples from the same speaker are not included in both development and test. The development set includes 80 participants (40 female, 40 male) containing the following distribution of labels: 241 negative, 305 neutral and 261 positive valence; and 148 low, 328 neutral and 331 high arousal. The test set includes 20 participants (10 female, 10 male) with the distribution of labels that follows: 57 negative, 62 neutral and 73 positive valence; and 13 low, 70 neutral and 109 high arousal. Files <strong>baseline_speech.ipynb</strong> and <strong>baseline_text.ipynb</strong> contain the code used to create the baseline emotion recognition models based on speech and text, respectively. The actual trained models for valence and arousal prediction are provided in folders <strong>models_speech</strong> and <strong>models_text</strong>. </li> </ul> <p><em>Audio files in “Lecture” and “Emotions” are only provided to the users that complete the agreement file in section Usage Notes. Audio files are in Ogg Vorbis format at 16-bit and 44.1 kHz or 48 kHz. The total size of the “Audios” folder is about 213 MB. </em></p> <h2>Usage Notes</h2> <p>All the data included in the EMOVOME database is publicly available under the Creative Commons Attribution 4.0 International license. The only exception is the original raw audio files, for which an additional step is required as a security measure to safeguard the speakers' privacy. To request access, interested authors should first complete and sign the agreement file <strong>EMOVOME_agreement.pdf</strong> and send it to the corresponding author (<em><a href="mailto:jamarmo@htech.upv.es" target="_blank" rel="noopener">jamarmo@htech.upv.es</a></em>). The data included in the EMOVOME database is expected to be used for research purposes only. Therefore, the agreement file states that the authors are not allowed to share the data with profit-making companies or organisations. They are also not expected to distribute the data to other research institutions; instead, they are suggested to kindly refer interested colleagues to the corresponding author of this article. By agreeing to the terms of the agreement, the authors also commit to refraining from publishing the audio content on the media (such as television and radio), in scientific journals (or any other publications), as well as on other platforms on the internet. The agreement must bear the signature of the legally authorised representative of the research institution (e.g., head of laboratory/department). Once the signed agreement is received and validated, the corresponding author will deliver the "Audios" folder containing the audio files through a download procedure. A direct connection between the EMOVOME authors and the applicants guarantees that updates regarding additional materials included in the database can be received by all EMOVOME users.</p> <p> </p> <p> </p>
A Dataset of Polarities and Emotions from Brazilian Portuguese Play Store Reviews
<p>User reviews play a crucial role in shaping consumer perceptions and guiding decision-making processes in the digital marketplace. With the rise of mobile applications, platforms like the Google Play Store serve as hubs for users to express their opinions and experiences with various apps and services. Understanding the polarities and emotions conveyed in these reviews provides valuable insights for developers, marketers, and researchers alike.</p> <p>The dataset consists of user reviews collected from the "Trending" section of the Google Play Store in May 2023. A total of 300 reviews were gathered for each of the top 10 most downloaded applications during this period. Each review in the dataset has been meticulously labeled for polarity, categorizing sentiments as positive, negative, or neutral, and emotion, encompassing a range of emotional responses such as happiness, sadness, surprise, fear, disgust and anger.</p> <p>Additionally, it's worth noting that this dataset underwent a rigorous annotation process. Three annotators independently classified the reviews for polarity and emotion. Afterward, they reconciled any discrepancies through discussion and arrived at a consensus for the final annotations. This ensures a high level of accuracy and reliability in the labeling process, providing researchers and practitioners with trustworthy data for analysis and decision-making.</p> <p>It's important to highlight that all reviews in this dataset are in Brazilian Portuguese, reflecting the specific linguistic and cultural nuances of the Brazilian market. By leveraging this dataset, stakeholders gain access to a robust resource for exploring user sentiment and emotion within the context of popular mobile applications in Brazil.</p>
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems
<p>This is the dataset created for the paper, "EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems" (https://arxiv.org/abs/2109.04919).</p> <p>EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset (https://github.com/budzianowski/multiwoz). It contains more than 11K task-oriented dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. There are 7 emotion labels, which are adapted from the OCC emotion models.</p> <p>For data format and label definition, please refer to README.md. </p>
Study-Data: Longitudinal Study about the relationship between computer mouse usage and emotional states
<p>This Dataset contains the raw data files of the longitudinal study about the relationship between computer mouse usage and emotional states. The files are gzipped json files.</p> <p>There are 3 separate data files.</p> <ol> <li>The self-directed mouse usage data recorded during a 5 minute interval of regular computer use (FreeMouse_dataset)</li> <li>The mouse usage data during the point-and-click task (MouseTask_dataset)</li> <li>The sociodemographics of the dataset (sociodem_dataset)</li> </ol> <p>Note that the sociodemographic data is also included in the FreeMouse dataset as well as in the MouseTask dataset.</p> <p>For any questions about the dataset, contact: paul.freihaut@psychologie.uni-freiburg.de</p>
SMARTED: SMARTwatch Emotion Dataset
<p>The SMARTED (SMARTwatch Emotion Dataset) dataset contains physiological data collected in three groups of people enrolled in a university research study.</p> <p>Refer to the README for further details.</p> <p> </p>
Replication package for "Teams and Individuals in Standard Auction Formats: Decision and Emotions"
<p>This is the replication package for the paper "Teams and Individuals in Standard Auction Formats: Decision and Emotions" by Maria Karmeliuk, Martin Kocher and Georg Schmidt, forthcoming in <em>Experimental Economics</em>.</p> <p>The package contains instructions and zTree code for running the laboratory experiment, as well as raw data from the experiment including the data from face recognition software FaceReader. It also contains Stata and R code for data cleaning, cleaned datasets and Stata code for replicating data analysis and statistics reported in the paper.</p>
Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
<p>Hi,KIA dataset is a shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains <strong>488 </strong>Wakeup Word speech. </p> <p>For more detailed information about the dataset, please refer to our paper: Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words</p> <p><strong>File Description</strong></p> <ul> <li><em><strong>wav/</strong></em>: wav files. <ul> <li>Filename f`{gender}_{pid}_{scene}_{trial}_{emotion}.wav` The first letter was used to express emotion.<br> </li> </ul> </li> <li><em><strong>annotation/</strong></em>: Information related to annotation and human validation of the entire speech</li> <li> <p><em><strong>split</strong></em>: 8fold data split with {train, valid, test}.csv </p> </li> <li> <p><em><strong>handcraft:</strong></em> Features used for data EDA and baseline performance</p> </li> <li> <p><em><strong>best_weights:</strong></em> wav2vec2.0 context network finetuning weights for re-implementation. Due to file size, we attach only fold M1, F5</p> </li> </ul> <p> </p> <p><strong>Reference</strong></p> <ul> </ul> <p>Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words [[ArXiv](https://arxiv.org/abs/2211.03371)]</p> <p>```<br> @inproceedings{kim2022hi,<br> title={Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words},<br> author={Taesu Kim, SeungHeon Doh, Gyunpyo Lee, Hyung seok Jun, Juhan Nam, Hyeon-Jeong Suk},<br> booktitle={Proceedings of the 14th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)},<br> year={2022}<br> }<br> ```</p>
Figure 2. Decision Making Model based on Emotions, Drives, and Different Memory Types
<p>The first model that is briefly introduced is an approach inspired by research findings in<br> neuro-psychology and psychoanalysis (see figure 2). The model considers the environment, the<br> body, and the brain/mind of an artificial being. The environment and the body are perceived via<br> sensors. (Re-)actions can be carried out via actuators. The model focuses on the decision-making<br> process of how to (re-)act according to situations currently perceived. In this decision-making<br> process, there are involved concepts like emotions, desires, drives, and different types of memory as<br> well as concepts like the Ego-Id-Superego model of Sigmund Freud. There are considered fast<br> reactions in form of reflexes and slower reactions that need reflection, thinking, and planning. For a<br> detailed model description, see [14, 17, 18].</p>
Figure 9. Classification accuracy regardless the ethnic group (Total accuracy 75%)-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our experiments show that, the impact of ethnic group on the accuracy of emotions<br> recognition is a positive where the accuracy of emotion recognition considering ethnic group is<br> 83.3% as shown in Figure 8, and we got 75% of accuracy regardless ethnic group as shown in<br> Figure 9.</p>
Figure 8. Classification accuracy of emotions considering the ethnic group-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>To study the accuracy of emotion recognition for our approach regardless the ethnic group<br> we used 108 images for the training representing six emotions of six persons. For testing, we used<br> 36 images representing six emotions of six persons.<br> On the other hand, to study the accuracy of emotion recognition for our approach<br> considering the ethnic group we used 36 images for training for each ethnic group representing six<br> emotions of six persons, and test the classifier by using 12 images representing six emotions of six<br> persons.</p>
EmoPairCompete - Physiological Signals Dataset for Emotion and Frustration Assessment under Team and Competitive Behaviours
<p>Please refer to the documentation at: https://github.com/DTUComputeStatisticsAndDataAnalysis/EmoPairCompete</p>
Supplementary Data of the project "Cross Cultural Tears - A Systematic Investigation of the Interpersonal Effects of Emotional Crying Across Different Cultural Backgrounds"
<p>Supplementary Data related to the publication: </p> <div> <p>Zickfeld, J. et al. (2021). Tears evoke the intention to offer social support: A systematic investigation of the interpersonal effects of emotional crying across 41 countries. Journal of Experimental Social Psychology, 95, 104137. <a href="https://doi.org/10.1016/j.jesp.2021.104137" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jesp.2021.104137 </a></p> </div>
Emozionalmente: a crowdsourced Italian speech emotional corpus
<p>This repository contains Emozionalmente: an extensive simulated speech emotional corpus in Italian. The dataset comprises 6,902 labeled samples acted out by 431 amateur actors, each verbalizing 18 different sentences to express the Big Six emotions (anger, disgust, fear, joy, sadness, surprise) plus neutrality. The labels represent the emotional communicative intention of the actors (i.e., the seven emotional states).</p> <p>Key details about the dataset:<br>- **Recording specifications**: The recordings were generally obtained with non-professional equipment. They are .wav files, mono-channel, with a sample size of 16 bits and a sample rate of 16,000 Hz. Each audio recording lasts 3.81 seconds on average (SD = 0.99 seconds).<br>- **Validation**: To validate the emotional content of the clips, 829 humans evaluated each audio recording, providing five evaluations per audio. The general Unweighted Average Recall (UAR) achieved by the evaluators was 66%, which is comparable to previous literature in the field.</p> <p>The repository includes the following additional resources:<br>1. **Demographic information**: Three .csv files describing the demographics of the actors and evaluators, as well as the emotions they expressed and recognized for each audio sample.<br>2. **Data splits**: A speaker-independent train-dev-test split, stratified by emotion, gender, and age.</p> <p>If you use this dataset, please cite the following paper:</p> <blockquote> <p>F. Catania, J. W. Wilke and F. Garzotto,<br><em>"Emozionalmente: A Crowdsourced Corpus of Simulated Emotional Speech in Italian,"</em><br>IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 1142–1155, 2025.<br>doi: <a target="_new" rel="noopener">10.1109/TASLPRO.2025.3540662</a></p> </blockquote>
Datasets for Crowd-based Requirements Engineering and aspect-based detection of learning-centered emotion from the text in Serbian language
<h1>Datasets for the paper "Enhancing Software and Learning with Serbian Student Feedback Corpora"</h1> <p>These datasets include student feedback on an Intelligent Tutoring System written in Serbian, annotated with categories for Crowd-based Requirements Engineering (CrowdRE) and aspect-based detection of learning-centered emotions. Four annotators manually annotated each sentence. </p> <p>The CrowdRE dataset includes two JSON files:</p> <ul> <li><strong>crowdre_english.json</strong> - annotated text with columns and classes written in English. Columns are: <ul> <li><em>Comment</em> - the entire student feedback</li> <li><em>Sentence</em> - sentence extracted from the feedback that was annotated</li> <li><em>Intention </em>- class representing the intention of the sentence</li> <li><em>Topic </em>- class representing the topic of the sentence</li> </ul> </li> <li><strong>crowdre_srpski.json</strong> - annotated text with columns and classes written in Serbian. Columns are:<br> <ul> <li><em>Komentar</em> - the entire student feedback</li> <li><em>Recenica </em>- sentence extracted from the entire feedback that was annotated</li> <li><em>Namera </em>- class representing the intention of the sentence</li> <li><em>Tema </em>- class representing the topic of the sentence.</li> </ul> </li> </ul> <p>The dataset for the aspect-based detection of learning-centered emotions includes two JSON files:</p> <ul> <li><strong>emotions_english.json </strong>- annotated text with columns and classes written in English. Columns are: <ul> <li><em>Comment </em>- the entire student feedback</li> <li><em>Sentence </em>- sentence extracted from the feedback that was annotated</li> <li><em>Aspect </em>- class representing the aspect of the sentence</li> <li><em>Emotion </em>- class representing the learning-centered emotion of the sentence</li> </ul> </li> <li><strong>emocije_srpski.json </strong>- annotated text with columns and classes written in Serbian. Columns are: <ul> <li><em>Komentar </em>- the entire student feedback</li> <li><em>Recenica</em> - sentence extracted from the entire feedback that was annotated</li> <li><em>Aspekt</em> - class representing the aspect of the sentence</li> <li><em>Emotion </em>- class representing the learning-centered emotion of the sentence.</li> </ul> </li> </ul> <p>Annotators annotated the dataset based on the annotation procedure and guidelines available <a href="https://github.com/Clean-CaDET/student-feedback-mining">here</a>. </p> <h2>Citation</h2> <p>If you use this in your research, please cite:</p> <blockquote> <p>Vidaković, D., Luburić, N., Kovačević, A., & Slivka, J. Enhancing software and learning with Serbian student feedback corpora. Language Resources & Evaluation (2025). https://doi.org/10.1007/s10579-025-09855-y</p> </blockquote> <p> </p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 15. Input Sources of an Affective Neuro-Symbol Representing an Emotion
<p>Based on the descriptions given above and the concept of neuro-symbolic information<br> processing outlined in Section 4.2, so-called “affective neuro-symbols” were defined for the<br> affective situation assessment architecture (see Figure 15). These affective neuro-symbols can<br> principally receive information from four different sources: (1) body states, (2) objects and events<br> perceived in the environment (external perception), (3) from other emotions and (4) cognitive<br> (reasoning) processes. An input from one of these sources can in certain circumstances already be<br> sufficient to activate an affective neuro-symbol. Different sources can either have an exhibitory or<br> inhibitory effect on the activation of an affective neuro-symbol.</p>
Figure 6. Interface of FFE program-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Points, Features Extraction and save all input information for the classifier (Features, Ethnic group,<br> Gender and emotion). Figure 6 shows the interface of FFE program.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.