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135 results for “multimodal dataset”
Multimodal Vision-Audio-Language Dataset
<p>The Multimodal Vision-Audio-Language Dataset is a large-scale dataset for multimodal learning. It contains 2M video clips with corresponding audio and a textual description of the visual and auditory content. The dataset is an ensemble of existing datasets and fills the gap of missing modalities.</p><p>Details can be found in the attached report.</p><h3><strong>Annotation</strong></h3><p>The annotation files are provided as Parquet files. They can be read using Python and the <i>pandas</i> and <i>pyarrow</i> library.</p><p>The split into train, validation and test set follows the split of the original datasets.</p><h4><strong>Installation</strong></h4><blockquote><p>pip install pandas pyarrow</p></blockquote><h4><strong>Example</strong></h4><blockquote><p>import pandas as pd<br>df = pd.read_parquet('annotation_train.parquet', engine='pyarrow')<br>print(df.iloc[0])</p></blockquote><blockquote><p>dataset AudioSet </p><p>filename train/---2_BBVHAA.mp3</p><p>captions_visual [a man in a black hat and glasses.]</p><p>captions_auditory [a man speaks and dishes clank.]</p><p>tags [Speech]</p></blockquote><h4><strong>Description</strong></h4><p>The annotation file consists of the following fields:<br><br><i>filename</i>: Name of the corresponding file (video or audio file)<br><i>dataset</i>: Source dataset associated with the data point<br><i>captions_visual</i>: A list of captions related to the visual content of the video. Can be NaN in case of no visual content<br><i>captions_auditory</i>: A list of captions related to the auditory content of the video<br><i>tags</i>: A list of tags, classifying the sound of a file. It can be NaN if no tags are provided</p><h3><strong>Data files</strong></h3><p>The raw data files for most datasets are not released due to licensing issues. They must be downloaded from the source. However, due to missing files, we provide them on request. Please contact us at schaumloeffel@em.uni-frankfurt.de</p>
MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks
<p><strong>Description: </strong>This MOCAS is a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin, which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.</p> <p><strong>Data Access:</strong> In order to protect the sensitive data and privacy of human subjects (e.g., physiological signals and facial views), only authorized researchers who consent to the End User License Agreement (EULA) are allowed to download the MOCAS. The researchers who want to access the MOCAS should visit our website and download the ELUA document;<a href="https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing"> https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing</a> After reviewing and filling the document up, they should email the signed ELUA document to <a href="mailto:info@smart-laboratory.org">info@smart-laboratory.org</a> and <a href="mailto:wonsu0513@gmail.com">wonsu0513@gmail.com </a>with Zenodo account and Reqeust Access via this repository (see the below file section). Then, our research group will review and grant their access to our Zenodo repository having the downsampled MOCAS dataset, subjective information, and supplementary codes used in this paper. For sharing the raw dataset, we will sequentially invite their email address used in the Zenodo and the EULA document to access raw dataset uploaded on an additional repository (Purdue BOX, https://purdue.box.com/v/mocas-dataset), due to huge size of the raw dataset.</p> <p><strong><a title="BibTeX">BibTeX </a>Citation:</strong></p> <div>Jo, W., Wang, R., Cha, G. E., Sun, S., Senthilkumaran, R. K., Foti, D., & Min, B. C. (2024). MOCAS: A multimodal dataset for objective cognitive workload assessment on simultaneous tasks. <em>IEEE Transactions on Affective Computing</em>.</div> <pre>or<br><br>@ARTICLE{jo2024mocas, author={Jo, Wonse and Wang, Ruiqi and Cha, Go-Eum and Sun, Su and Senthilkumaran, Revanth Krishna and Foti, Daniel and Min, Byung-Cheol}, journal={IEEE Transactions on Affective Computing}, title={MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks}, year={2025}, volume={16}, number={1}, pages={116-132}, doi={10.1109/TAFFC.2024.3414330}}</pre>
Multimodal Remote Sensing Dataset for Landslide Change Detection in Haiti
<p>The detection of the collapse of landslides trigerred by intense natural hazards, such as earthquakes and rainfall, allows rapid response to hazards which turned into disasters. The use of remote sensing imagery is mostly considered to cover wide areas and assess even more rapidly the threats. Yet, since optical images are sensitive to cloud coverage, their use is limited in case of emergency response. The proposed dataset is thus multimodal and targets the early detection of landslides following the disastrous earthquake which occurred in Haiti in 2021. It consists of SAR-optical pairs from Sentinel-1 and Sentinel-2 acquired shortly before and after the event. To encourage for machine and deep learning algorithms, the images are cropped into 1713 patches. Since Sentinel-1 SAR images are acquired both in ascending and descending passes, twice more patches are available in this modality. Due to the mountainous nature of the terrain, the images are provided together with a layover-shadow mask computed as described in [Meier <em>et al</em>, 1993]. This mask is destined to limit the impact of geometrical distortions on the algorithm. Similarly, Sentinel-2 optical images are provided with a cloud and cloud shadow mask. Landslide annotations are extracted from the TODO database from NASA, rasterized and croped similarly to the images. The dataset is aimed to be used whether for monomodal or multimodal approaches, and whether for monotemporal or bitemporal approaches.</p> <p>[Meier et al., 1993] Meier, E., Frei, U., and Nüesch, D. (1993). Precise Terrain Corrected Geocoded Images. SAR Geocoding: Data and Systems, pages 173–185. Publisher: Karlsruhe, Germany: Herbert Wichmann Verlag GmbH.</p>
Dataset related to article:MULTIMODAL DIGITAL HEALTH TREATMENTS FOR CHRONIC MIGRAINE ASSOCIATED WITH MEDICATION OVERUSE HEADACHE: A LITERATURE APPRAISAL AND RESULTS OF A SINGLE‑ARM OPEN TRIAL (THE BE‑HOME PROGRAM)
<p><strong><span>Variables related to baseline, months 3, 6 and 12 referred to headache frequency, medication intake, GSE, HIT-6, BDI-II, MSQ and PCS, of patients with CM and MOH who participated to the BE-HOME trial.</span></strong></p>
Iono_Electron Dataset for Multimodal Fusion and Spatiotemporal Predictive Learning
<p>Data supplementary material for <em><strong>Space Weather</strong></em> journal paper with the title: Channel Mixer Layer: Multimodal Fusion Towards Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content</p> <p> </p>
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Robosense BPearl LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with multiple sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Acquired from a Robosense Bpearl.</li> <li><strong>IMU Data:</strong> Available through the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Captured using a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, available at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>, along with the necessary <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a>.</p> <p><strong>Note:</strong> The transform for the LiDAR sensor is not included in the rosbag. To include it, manually add the following static transform to your ROS setup:<br><node pkg="tf" type="static_transform_publisher" name="rslidar_broadcaster" args="0.15 0 0.13 0 0.309017 0 base rslidar 0.1" /><br><br><strong>Important:</strong> There was a camera malfunction midway through the dataset collection.</p>
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Velarray M1600 LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with various sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Captured with a Velodyne Velarray M1600.</li> <li><strong>IMU Data:</strong> Available via the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Streamed from a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, which can be found at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>. Ensure that the <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a> is also installed.</p> <p><strong>Note:</strong> The LiDAR transform is not included in the rosbag file. To incorporate this transform, manually add the following static transform to your ROS setup:<br><br><node pkg="tf" type="static_transform_publisher" name="lidar_0_broadcaster" args="0.15 0 0.13 0 0 0 base lidar_0 0.1" /><br><br></p>
Multimodal Depression Dataset
<h1>描述</h1> <p>多模态抑郁数据集(Multimodal Depression Dataset)由中国科学院大学和中国科学院心理研究所计算网络心理学实验室收集。</p> <p>该数据集包含来自254名参与者的视频、音频、脑电图(EEG)和步态(Gait)数据。</p> <h2> </h2>
Multimodal Birds Song Dataset: TwoRadioBirds
<p>A multi-modal dataset containing recordings of an adult male and female Zebra Finch bird using a microphone and two accelerometer sensors. The dataset consists of eleven data files of 1 hr. duration each, out of which three are densely annotated, and eight are unlabelled. Each file contains data from a microphone (audio) and two accelerometers (body vibration) channels. The data in all channels are sampled at a rate of 24000 Hz.</p> <p>Link to paper (IEEE Xplore): <a href="https://ieeexplore.ieee.org/abstract/document/10094650">https://ieeexplore.ieee.org/abstract/document/10094650</a></p> <p>Link to paper (arxiV): <a href="https://arxiv.org/pdf/2211.09376.pdf">https://arxiv.org/pdf/2211.09376.pdf</a></p> <p>Link to dataset: <a href="https://doi.org/10.5281/zenodo.7253729">https://doi.org/10.5281/zenodo.7253729</a></p> <p><strong>Cite as:</strong> Kumar, S., Anshuman, B., Rüttimann, L., Hahnloser, R.H. and Arora, V., 2023, June. Balanced Deep CCA for Bird Vocalization Detection. In <em>ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</em> (pp. 1-5). IEEE.</p>
MIntRec2. 0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
<p><span>MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 high-quality dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes, across text, video, and audio modalities. In addition to more than 9,300 in-scope samples, it also includes over 5,700 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world open scenarios, enhancing its practical applicability.</span> <span>This dataset will be released under the CC BY-NC-SA 4.0 license.</span></p>
Construction and Application of the Visualization Training Platform Based on a Multimodal Standardized Dataset for Pain Assessment in Critically Ill Children
ClinicalTrials.gov study NCT06431802. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Dataset for the project "Corticocortical signal transmission and brain connectivity alterations at prodromal stage and during the progression of Alzheimer's disease: A multimodal approach of TMS-EEG and advanced MRI" (GR-2016-02364132)
<p><strong>Aim 1:</strong></p> <p><u>Validation of STT as measure of WM integrity and as diagnostic marker of AD</u>. The first step to test the potential of the STT as AD marker is to establish if the SST can differentiate between patients (LOAD, aMCI) and healthy individuals and its relationship with clinical symptoms and with well-established measures of connectivity.</p> <p> <strong>Aim 2:</strong></p> <p><u>Connectivity changes, evaluated as in aim 1, as prognostic marker of disease progression</u>. Another crucial point is to establish if STT can capture brain connectivity alterations with disease progression, if they parallel WM integrity alterations and cognitive decline. Moreover, the potential contribution of STT as prognostic marker can be established by investigating which measure, or combination of measures, of brain connectivity at the baseline best predicts the conversion to AD in aMCI cases.</p> <p> <strong>Aim3:</strong></p> <p><u>Connectivity changes in EOAD patients.</u> The comparison of connectivity alterations between EOAD and LOAD at the baseline and during the disease progression will be a crucial step to establish the efficacy of STT as a marker of differential diagnosis in atypical forms of AD.</p> <p> </p> <p><strong>The present dataset contains the following files and folders:</strong></p> <p> </p> <p>File: <strong>Demographics_allparticipants.xlsx</strong> contains demographic and clinical data of the entire sample</p> <p>File: <strong>Hippocampal volume.xlsx</strong> data used to extract hippocampal atrophy as AD biomarker</p> <p> </p> <p>Folder: <strong>Aim1</strong></p> <p>- <strong>READ ME</strong> AIM1.txt explains folder structure</p> <p>- <strong>TMS-EEG</strong> data of HC, MCI and LOAD at T0 are organized in 3 files with TMS-Evoked Potentials (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file)</p> <p>- <strong>Neuropsychological</strong> data of HC, MCI and LOAD at T0 (.xlsx format)</p> <p>- <strong>fMRI</strong> folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T0</p> <p>- <strong>DTI</strong> data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0</p> <p> </p> <p>- Folder: <strong>Aim2</strong></p> <p>- <strong>READ ME</strong> AIM2.txt explains folder structure</p> <p>- <strong>TMS-EEG</strong> data are organized in one file that includes TMS-Evoked Potentials of HC, MCI and LOAD at T0 and T1 (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file)</p> <p>- <strong>Neuropsychological</strong> data of HC, MCI and LOAD at T1 (.xlsx format)</p> <p>- <strong>fMRI</strong> folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T1</p> <p>- <strong>DTI</strong> data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0 and T1</p> <p> </p> <p>Folder: <strong>Aim3</strong></p> <p> <strong>READ ME</strong> AIM2.txt explains folder structure</p> <p>- <strong>TMS-EEG</strong> data are organized in two files that includes TMS-Evoked Potentials of EOAD and LOAD at T0 and T1 (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file)</p> <p>- <strong>Neuropsychological</strong> data of EOAD and LOAD at T0 and T1 (.xlsx format)</p> <p>- <strong>fMRI</strong> folder contains coordinates of the hotspot for the stimulated functional networks of EOAD and LOAD at T0 and T1</p> <p>- <strong>DTI</strong> data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of EOAD and LOAD at T0 and T1</p> <p> </p> <p>Restricted Access Conditions: Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.</p>
CMFeed: A Benchmark Dataset for Controllable Multimodal Feedback Synthesis
<p><strong>Overview</strong><br>The Controllable Multimodal Feedback Synthesis (CMFeed) Dataset is designed to enable the generation of sentiment-controlled feedback from multimodal inputs, including text and images. This dataset can be used to train feedback synthesis models in both uncontrolled and sentiment-controlled manners. Serving a crucial role in advancing research, the CMFeed dataset supports the development of human-like feedback synthesis, a novel task defined by the dataset's authors. Additionally, the corresponding feedback synthesis models and benchmark results are presented in the <a href="https://github.com/MIntelligence-Group/CMFeed/" target="_blank" rel="noopener">associated code and research publication</a>. <br><br><em>Task Uniqueness</em>: The task of controllable multimodal feedback synthesis is unique, distinct from LLMs and tasks like VisDial, and not addressed by multi-modal LLMs. LLMs often exhibit errors and hallucinations, as evidenced by their auto-regressive and black-box nature, which can obscure the influence of different modalities on the generated responses [<a href="https://www.nature.com/articles/s41586-024-07421-0" target="_blank" rel="noopener">Ref1</a>; <a href="https://arxiv.org/abs/2311.05232" target="_blank" rel="noopener">Ref2</a>]. Our approach includes an interpretability mechanism, as detailed in the supplementary material of the corresponding <a href="https://github.com/MIntelligence-Group/CMFeed/" target="_blank" rel="noopener">research publication</a>, demonstrating how metadata and multimodal features shape responses and learn sentiments. This controllability and interpretability aim to inspire new methodologies in related fields.<br><br><strong>Data Collection and Annotation</strong><br>Data was collected by crawling Facebook posts from major news outlets, adhering to ethical and legal standards. The comments were annotated using four sentiment analysis models: FLAIR, SentimentR, RoBERTa, and DistilBERT. Facebook was chosen for dataset construction because of the following factors:<br>• Facebook was chosen for data collection because it uniquely provides metadata such as news article link, post shares, post reaction, comment like, comment rank, comment reaction rank, and relevance scores, not available on other platforms. <br>• Facebook is the most used social media platform, with 3.07 billion monthly users, compared to 550 million Twitter and 500 million Reddit users. [<a href="https://en.wikipedia.org/wiki/List_of_social_platforms_with_at_least_100_million_active_users" target="_blank" rel="noopener">Ref</a>] <br>• Facebook is popular across all age groups (18-29, 30-49, 50-64, 65+), with at least 58% usage, compared to 6% for Twitter and 3% for Reddit. [<a href="https://sproutsocial.com/insights/new-social-media-demographics/" target="_blank" rel="noopener">Ref</a>]. Trends are similar for gender, race, ethnicity, income, education, community, and political affiliation [<a href="https://pewresearch.org/internet/fact-sheet/social-media/" target="_blank" rel="noopener">Ref</a>] <br>• The male-to-female user ratio on Facebook is 56.3% to 43.7%; on Twitter, it's 66.72% to 23.28%; Reddit does not report this data. [<a href="https://khoros.com/resources/social-media-demographics-guide" target="_blank" rel="noopener">Ref</a>]</p> <p><em>Filtering Process</em>: To ensure high-quality and reliable data, the dataset underwent two levels of filtering:<br>a) Model Agreement Filtering: Retained only comments where at least three out of the four models agreed on the sentiment.<br>b) Probability Range Safety Margin: Comments with a sentiment probability between 0.49 and 0.51, indicating low confidence in sentiment classification, were excluded.<br>After filtering, 4,512 samples were marked as XX. Though these samples have been released for the reader's understanding, they were not used in training the feedback synthesis model proposed in the corresponding research paper.<br><br><strong>Dataset Description</strong><br>• Total Samples: 61,734<br>• Total Samples Annotated: 57,222 after filtering.<br>• Total Posts: 3,646<br>• Average Likes per Post: 65.1<br>• Average Likes per Comment: 10.5<br>• Average Length of News Text: 655 words<br>• Average Number of Images per Post: 3.7<br><br><strong>Components of the Dataset</strong><br>The dataset comprises two main components:<br>• <em>CMFeed.csv</em> File: Contains metadata, comment, and reaction details related to each post.<br>• <em>Images</em> Folder: Contains folders with images corresponding to each post.<br><br><strong>Data Format and Fields of the CSV File</strong><br>The dataset is structured in CMFeed.csv file along with corresponding images in related folders. This CSV file includes the following fields:<br>• <em>Id</em>: Unique identifier <br>• <em>Post</em>: The heading of the news article.<br>• <em>News_text</em>: The text of the news article.<br>• <em>News_link</em>: URL link to the original news article.<br>• <em>News_Images</em>: A path to the folder containing images related to the post.<br>• <em>Post_shares</em>: Number of times the post has been shared.<br>• <em>Post_reaction</em>: A JSON object capturing reactions (like, love, etc.) to the post and their counts.<br>• <em>Comment</em>: Text of the user comment.<br>• <em>Comment_like</em>: Number of likes on the comment.<br>• <em>Comment_reaction_rank</em>: A JSON object detailing the type and count of reactions the comment received.<br>• <em>Comment_link</em>: URL link to the original comment on Facebook.<br>• <em>Comment_rank</em>: Rank of the comment based on engagement and relevance.<br>• <em>Score</em>: Sentiment score computed based on the consensus of sentiment analysis models.<br>• <em>Agreement</em>: Indicates the consensus level among the sentiment models, ranging from -4 (all negative) to 4 (all positive). 3 negative and 1 positive will result into -2 and 3 positives and 1 negative will result into +2.<br>• <em>Sentiment_class</em>: Categorizes the sentiment of the comment into 1 (positive) or 0 (negative).<br><br><strong>More Considerations During Dataset Construction<br></strong>We thoroughly considered issues such as the choice of social media platform for data collection, bias and generalizability of the data, selection of news handles/websites, ethical protocols, privacy and potential misuse before beginning data collection. While achieving completely unbiased and fair data is unattainable, we endeavored to minimize biases and ensure as much generalizability as possible. Building on these considerations, we made the following decisions about data sources and handling to ensure the integrity and utility of the dataset:<em><br><br>• Why not merge data from different social media platforms? </em>We chose not to merge data from platforms such as Reddit and Twitter with Facebook due to the lack of comprehensive metadata, clear ethical guidelines, and control mechanisms—such as who can comment and whether users' anonymity is maintained—on these platforms other than Facebook. These factors are critical for our analysis. Our focus on Facebook alone was crucial to ensure consistency in data quality and format.</p> <p><em>• Choice of four news handles</em><strong>:</strong> We selected four news handles—BBC News, Sky News, Fox News, and NY Daily News—to ensure diversity and comprehensive regional coverage. These news outlets were chosen for their distinct regional focuses and editorial perspectives: BBC News is known for its global coverage with a centrist view, Sky News offers geographically targeted and politically varied content learning center/right in the UK/EU/US, Fox News is recognized for its right-leaning content in the US, and NY Daily News provides left-leaning coverage in New York. Many other news handles such as NDTV, The Hindu, Xinhua, and SCMP are also large-scale but may contain information in regional languages such as Indian and Chinese, hence, they have not been selected. This selection ensures a broad spectrum of political discourse and audience engagement.</p> <p><em>• Dataset Generalizability and Bias</em><strong>:</strong> With 3.07 billion of the total 5 billion social media users, the extensive user base of Facebook, reflective of broader social media engagement patterns, ensures that the insights gained are applicable across various platforms, reducing bias and strengthening the generalizability of our findings. Additionally, the geographic and political diversity of these news sources, ranging from local (NY Daily News) to international (BBC News), and spanning political spectra from left (NY Daily News) to right (Fox News), ensures a balanced representation of global and political viewpoints in our dataset. This approach not only mitigates regional and ideological biases but also enriches the dataset with a wide array of perspectives, further solidifying the robustness and applicability of our research.</p> <p><em>• Dataset size and diversity:</em> Facebook prohibits the automatic scraping of its users' personal data. In compliance with this policy, we manually scraped publicly available data. This labor-intensive process requiring around 800 hours of manual effort, limited our data volume but allowed for precise selection. We followed ethical protocols for scraping Facebook data , selecting 1000 posts from each of the four news handles to enhance diversity and reduce bias. Initially, 4000 posts were collected; after preprocessing (detailed in Section 3.1), 3646 posts remained. We then processed all associated comments, resulting in a total of 61734 comments. This manual method ensures adherence to Facebook’s policies and the integrity of our dataset.</p> <p><strong>Ethical considerations, data privacy and misuse prevention<br></strong>The data collection adheres to Facebook’s ethical guidelines [<a href="https://developers.facebook.com/terms/" target="_blank" rel="noopener">Ref</a>]. We manually scraped publicly available data in compliance with Facebook's ethical guidelines prohibiting automatic scraping [<a href="https://www.facebook.com/help/463983701520800" target="_blank" rel="noopener">Ref</a>]. We collected data that is publicly available, specifically corresponding to news articles that are publicly accessible following the protocols for ethically scraping Facebook data [<a href="https://webscraping.blog/how-to-scrape-facebook/" target="_blank" rel="noopener">Ref</a>]. The human-generated comments are included without identifying information. Aiming to prevent potential misuse, we have proactively designed our feedback synthesis system with an integral interpretability module. This feature is crucial as it not only helps in explaining how decisions are made within the system but also in detecting and preventing any misuse, such as the creation of misleading or manipulative content. Our original motivation for integrating this technology was to ensure that it is used responsibly and ethically, enhancing its positive impact while minimizing risks. By focusing on developing robust and transparent systems, we aim to foster trust and encourage the responsible use of technology in line with our ethical commitments.<br><br><strong>Code and Citation</strong><br>• Code Repository: <a href="https://github.com/MIntelligence-Group/CMFeed/" target="_blank" rel="noopener">https://github.com/MIntelligence-Group/CMFeed/</a><br>• Citing the Dataset: Users of the dataset should cite the corresponding paper described at the above GitHub Repository.<br><br><strong>License & Access</strong><br>• This dataset is released for academic research only and is free to researchers from educational or research institutes for non-commercial purposes.<br>• Note that you are downloading this corpus at your own risk. No guarantee is provided, e.g. regarding the goodness of the corpus nor towards any subsequent effects. You may use it free of charge, and modify it as you wish, but clearly specify modifications if you pass modified material on.<br><br><strong>Contact</strong><br>Please send any questions about this dataset to:<br>• Puneet Kumar (puneet.kumar@oulu.fi),<br>• Sarthak Malik (sarthak_m@mt.iitr.ac.in),<br>• Balasubramanian Raman (bala@cs.iitr.ac.in),<br>• Xiaobai Li (xiaobai.li@zju.edu.cn).</p>
Dataset for the project "The effects of a multimodal approach for the treatment of Primary Progressive Aphasia" (GR-2018-12365105)
<p> </p> <p><strong>Dataset for the project “The effects of a multimodal approach for the treatment of Primary Progressive Aphasia” (GR-2018-12365105)</strong><br>Manenti Rosa (https://orcid.org/0000-0002-5955-2323); Premi Enrico (https://orcid.org/0000-0002-4342-6963 </p> <p>Dataset for the project “The effects of a multimodal approach for the treatment of Primary Progressive Aphasia” Italian Ministry of Health, Italy, Ricerca Finalizzata Young Researcher (Grant GR-2018-12365105)</p> <p><strong>Specific Aim 1</strong></p> <p>Evaluate the specific short-term and long-term effects of active tDCS over the dorsolateral prefrontal cortex (DLPFC) combined with language training vs. placebo DLPFC-tDCS with language training vs. active DLPFC-tDCS alone. In particular, we aim to establish if the combined use of active tDCS and language training in avPPA patients can induce an improvement in a) cognitive and functional performances and b) a change in molecular markers such as neurogranin.</p> <p><strong>Specific Aim 2</strong></p> <p>a) Evaluate the brain activity changes related to the intervention in targeted brain regions to establish BOLD signal fluctuations (fMRI);<br>b) Evaluate the variations in spontaneous hemodynamic cortical activity and reactivity with a fNIRS assessment. </p> <p>To this aim, we will perform fMRI scanning and fNIRS assessment before and after the application of the three therapy protocols to provide real-time measures of whole-brain activity modulation that are induced in the activated network.</p> <p>The present dataset contains the following data:</p> <p>• <strong> RawData_Aim1a [Unpublished Data]</strong><br>o RawData_Aim1a_Clinical_Neuropsychiological_Assessment: a dataset (excel file) containing the variables referred to overall cognitive and functional performance at timing T0 (baseline), T1 (post-treatment) and T2 (follow-up) </p> <p><strong>• RawData_Aim1b_ Molecular markers [Unpublished Data]</strong><br>o RawData_Aim1b_ Molecular markers: an excel file containing:<br> - RawData_Aim1b_ Plasma_BDNF_Neurogranin: an excel sheet containing a dataset with the following variables:<br> Sample (Col. A);<br> Raw Values Plasma BDNF T0 (Col. B-D);<br> Raw Values Plasma BDNF T1 (Col. E-G);<br> Raw Values Plasma Neurogranin T0 (Col. H-J); <br> Raw Values Plasma Neurogranin T1 (Col. K-M). <br> - RawData_Aim1b_ RawData_BDNF_Simoa_Test: an excel sheet containing a dataset with the following variables:<br> Sample (Col. A);<br> Raw Simoa Values Plasma BDNF T0 (Col. B-D); <br> Raw Simoa Values Plasma BDNF T1 (Col. E-G).</p> <p><strong>• RawData_Aim2a_ MRI [Unpublished Data]</strong><br>o RawData_Aim2a_ MRI: a folder containing:<br> - RawData_Aim2a_structural_MRI: a folder containing images referred on structural changes from timing T0 (baseline) to T1 (post-treatment) <br> - RawData_Aim2a_functional_MRI: a folder containing images referred on functional changes from timing T0 (baseline) to T1 (post-treatment) </p> <p><strong>• RawData_Aim2b_ fNIRS [Unpublished Data]</strong><br>o RawData_Aim2b_ fNIRS: a folder containing:<br> - RawData_Aim2b_ fNIRS: raw fNIRS files for each subject at timing T0 (baseline), T1 (post-treatment) and T2 (follow-up) and a dataset (excel file) containing the following variables:<br> Code (Col. A)<br> Age (Col. B)<br> Education (Col. C)<br> Gender (Col. D)<br> fNIRS condition at timing T0 (baseline) (Col. E)<br> Duration of conditions at timing T0 (baseline) (Col. F, G, H, I)<br> fNIRS condition at timing T1 (post-treatment) (Col. J)<br> Duration of conditions at timing T1 (post-treatment) (Col. K, L, M, N)<br> fNIRS condition at timing T2 (follow-up) (Col. O)<br> Duration of conditions at timing T2 (follow-up) (Col. P, Q, R, S)</p> <p><strong>Preliminary Data [Published Data]</strong><br>Preliminary results of the project are published in:<br>• Premi&Dukart et al., Human Brain Map 2023 (doi: 10.1002/hbm.26206) and the raw data supporting the results are published in the JuSpace toolbox at https://github.com/juryxy/JuSpace</p> <p>• Premi&Cotelli et al., Front Aging Neurosci. 2022 (doi: 10.3389/fnagi.2022.942095) and the raw data supporting the results are published in the Zenodo Data Repository at doi: 10.5281/zenodo.6874926 and here reported:<br> - RawData_ Premi&Cotelli: a dataset (excel file) containing variables referred to correlations between grey matter volume and language deficits in avPPA at timing T0 </p> <p><strong>You need to satisfy these conditions in order for your request to be accepted:</strong><br><strong>Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication. </strong></p> <p> </p>
Longitudinal multimodal MRI dataset of experimental ischemic stroke in male and female rats
<p>Preclinical MRI dataset (rat) accompanying https://doi.org/10.1177/0271678X231208993</p>
K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations
<p>ABSTRACT: Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of naturalistic affective interaction data. Most existing emotion datasets do not support studying idiosyncratic emotions arising in the wild as they were collected in constrained environments. Therefore, studying emotions in the context of social interactions requires a novel dataset, and K-EmoCon is such a multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. Distinct from previous datasets, it includes emotion annotations from all three available perspectives: self, debate partner, and external observers. Raters annotated emotional displays at intervals of every 5 seconds while viewing the debate footage, in terms of arousal-valence and 18 additional categorical emotions. The resulting K-EmoCon is the first publicly available emotion dataset accommodating the multiperspective assessment of emotions during social interactions.</p> <pre><code>+---------------------------------------+ | Changelog (last updated: Jul 7, 2020) | +---------------------------------------+ * Version 1.0.0 (Jul 7, 2020): - Updated emotion_annotations.tar.gz: - Updated aggregated external annotations to support the reproduction of technical validation results. * Version 0.2.0 (May 11, 2020): - Added data_quality_tables.tar.gz - Updated emotion_annotations.tar.gz - Newly added aggregated external annotations. - Updated metadata.tar.gz: - Added a new column to data_availability.csv to show the availability of aggregated external annotations. * Version 0.1.0 (Apr 25, 2020): - Added debate_audios.tar.gz - Added debate_recordings.tar.gz - Added e4_data.tar.gz - Added emotion_annotations.tar.gz (self, partner, external) - Added metadata.tar.gz - Added neurosky_polar_data.tar.gz</code></pre> <p> </p>
MuFoRa – A Multimodal Dataset of Traffic Elements Under Controllable and Measured Adverse Weather Conditions of Fog and Rain
<div> <p>This is a novel multimodal dataset of traffic elements consisting of image and point cloud files in typical adverse weather situations especially suited for autonomous driving sensors and algorithms performance characterization. The specialty of this dataset arises from parametric acquisition under controllable and measured fog (quantified as visibility) and rain intensity conditions in the indoor test facility of CARISMA - THI in collaboration with HM. </p> <p> </p> </div>
ROS2 bag multimodal dataset acquired in a Portuguese forest with a handheld sensing platform for Eucalyptus selective thinning
<p>Dataset of several ROS2 bags acquired using a handheld sensing platform in a forest area in Melres, Portugal (41.08559152851395, -8.401160652897337).<br> <br> The bags are composed by Point Clouds, RGB images, Stereo images, Depth images and IMU data of three sensors:</p> <ul> <li>Velodyne Velarray M1600</li> <li>Livox Mid-70</li> <li>OAK-D</li> </ul>
Link to dataset related to article "Multimodal single-cell profiling of intrahepatic cholangiocarcinoma defines hyperactivated Tregs as a potential therapeutic target "
<p>This record contains raw data related to article “Multimodal single-cell profiling of intrahepatic cholangiocarcinoma defines hyperactivated Tregs as a potential therapeutic target"</p> <p>Abstract:</p> <p><strong>Background & aims: </strong> The landscape and function of the immune infiltrate of intrahepatic cholangiocarcinoma (iCCA), a rare, yet aggressive tumor of the biliary tract, remains poorly characterized, limiting development of successful immunotherapies. Herein, we aimed to define the molecular characteristics of tumor-infiltrating leukocytes with a special focus on CD4+ regulatory T cells (Tregs).</p> <p><strong>Methods: </strong> We used high-dimensional single-cell technologies to characterize the T-cell and myeloid compartments of iCCA tissues, comparing these with their tumor-free peritumoral and circulating counterparts. We further used genomics and cellular assays to define the iCCA-specific role of a novel transcription factor, mesenchyme homeobox 1 (MEOX1), in Treg biology.</p> <p><strong>Results: </strong> We found poor infiltration of putative tumor-specific CD39+ CD8+ T cells accompanied by abundant infiltration of hyperactivated CD4+ Tregs. Single-cell RNA-sequencing identified an altered network of transcription factors in iCCA-infiltrating compared to peritumoral T cells, suggesting reduced effector functions by tumor-infiltrating CD8+ T cells and enhanced immunosuppression by CD4+ Tregs. Specifically, we found that expression of MEOX1 was highly enriched in tumor-infiltrating Tregs, and demonstrated that MEOX1 overexpression is sufficient to reprogram circulating Tregs to acquire the transcriptional and epigenetic landscape of tumor-infiltrating Tregs. Accordingly, enrichment of the MEOX1-dependent gene program in Tregs was strongly associated with poor prognosis in a large cohort of patients with iCCA.</p> <p><strong>Conclusions: </strong> We observed abundant infiltration of hyperactivated CD4+ Tregs in iCCA tumors along with reduced CD8+ T-cell effector functions. Interfering with hyperactivated Tregs should be explored as an approach to enhance antitumor immunity in iCCA.</p> <p><strong>Lay summary: </strong> Immune cells have the potential to slow or halt the progression of tumors. However, some tumors, such as intrahepatic cholangiocarcinoma, are associated with very limited immune responses (and infiltration of cancer-targeting immune cells). Herein, we show that a specific population of regulatory T cells (a type of immune cell that actually suppresses the immune response) are hyperactivated in intrahepatic cholangiocarcinoma. Targeting these cells could enable cancer-targeting immune cells to act more effectively and should be looked at as a potential therapeutic approach to this aggressive cancer type.</p>
Dataset for the project "Tactile mirror system in borderline personality disorder: a multimodal approach to study brain connectivity and plasticity" (SG-2019-12370473)
<p><strong>Aim 1:</strong></p> <p>Assessment of TaMS alterations in BPD at behavioral and neurophysiological level, focusing on effective connectivity pattern by means of TMS-EEG, and its relationship with empathic abilities (Exp. 1).</p> <p><strong>Aim 2:</strong></p> <p>Assessment of plasticity mechanisms within TaMS in BPD by means of a cross-modal PAS protocol and their relationship with empathic abilities (Exp. 2).</p> <p> </p> <p><strong>The present dataset contains the following files and folders:</strong></p> <p>Folder: <strong>Aim1</strong></p> <p>- <strong>READ ME</strong> AIM1.txt explains folder structure and file content details.</p> <p>- File: <strong>SG2019_Aim1_Demographics.xlsx </strong>contains demographic data. </p> <p>- File <strong>SG2019_Aim1_Empathy.xlsx </strong>contains data on the empathic levels obtained by self-report questionnaires.</p> <p>- File:<strong> SG2019_Aim1_rMT.xlsx </strong>contains the rMT measured to set TMS intensity.</p> <p>- File <strong>SG2019_Aim1_VTSC.xlsx </strong>contains data on behavioral performance (RTs and number of errors).</p> <p>- Subfolder: <strong>TMS-Evoked Potentials</strong>. Data are organized in 6 files per group according to trial type (.mat format).</p> <p>- Subfolder: <strong>Event-Related Potentials</strong>. Data are organized in 3 files per group according to trial type (.mat format).</p> <p>- Subfolder: <strong>Time-frequency Pattern</strong>. Data are organized in 3 files per group according to trial type (.mat format).</p> <p> </p> <p>Folder: <strong>Aim2</strong></p> <p>- <strong>READ ME</strong> AIM2.txt explains folder structure and file content details.</p> <p>- File: <strong>SG2019_Aim2_Demographics.xlsx </strong>contains demographic data. </p> <p>- File <strong>SG2019_Aim2_Empathy.xlsx </strong>contains data on the empathic levels obtained by self-report questionnaire.</p> <p>- File:<strong> SG2019_Aim2_rMT.xlsx </strong>contains the rMT measured to set TMS intensity in the two experimental sessions and which session was performed first.</p> <p>- File <strong>SG2019_Aim2_VTSC.xlsx </strong>contains data on behavioral performance (RTs and number of errors) in the two experimental sessions.</p> <p> </p> <p><strong>Acronym list:</strong></p> <p><u>BPD</u>: borderline personality disorder</p> <p><u>EEG</u>: electroencephalography</p> <p><u>HC</u>: healthy controls</p> <p><u>PAS</u>: paired associative stimulation</p> <p><u>pw-BPD</u>: patients with BPD</p> <p><u>rMT</u>: resting motor threshold</p> <p><u>TaMS</u>: tactile mirror system</p> <p><u>TMS</u>: transcranial magnetic stimulation</p> <p><u>VTSC</u>: visuo-tactile spatial congruity task</p> <p> </p> <p>Restricted Access Conditions: Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.</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.