Find research datasets worth reusing

restrictedcc-by-4.0Aug 2022View details →

Spectral data obtained by placing a multimodal interference filter with and without exfoliated graphite coating in a ring-shaped laser cavity

This dataset contains the files obtained from the optical spectrum analyzer, by inserting a multimodal interference filter (MMIF) coated with graphite oxide into the ring-shaped laser cavity. The data, according to the file name, is divided into three parts. In one dataset are the spectrum of the cavity, at different pump powers, before inserting the MMIF. The other data set has the spectrum at different pump powers, with the MMIF without graphite oxide coating in the laser cavity. The last data set contains the spectrum at different pump powers, with the MMIF coated with graphite oxide inside the laser cavity.

restrictedcc-by-nc-4.0May 2024View details →

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)

Aim 1: Validation of STT as measure of WM integrity and as diagnostic marker of AD. 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.  Aim 2: Connectivity changes, evaluated as in aim 1, as prognostic marker of disease progression. 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.  Aim3: Connectivity changes in EOAD patients. 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.   The present dataset contains the following files and folders:   File: Demographics_allparticipants.xlsx contains demographic and clinical data of the entire sample File: Hippocampal volume.xlsx data used to extract hippocampal atrophy as AD biomarker   Folder: Aim1 -        READ ME AIM1.txt explains folder structure -        TMS-EEG 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) -        Neuropsychological data of HC, MCI and LOAD at T0 (.xlsx format) -        fMRI folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T0 -        DTI data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0   -         Folder: Aim2 -        READ ME AIM2.txt explains folder structure -        TMS-EEG 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) -        Neuropsychological data of HC, MCI and LOAD at T1 (.xlsx format) -        fMRI folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T1 -        DTI data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0 and T1   Folder: Aim3  READ ME AIM2.txt explains folder structure -        TMS-EEG 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) -        Neuropsychological data of EOAD and LOAD at T0 and T1 (.xlsx format) -        fMRI folder contains coordinates of the hotspot for the stimulated functional networks of EOAD and LOAD at T0 and T1 -        DTI data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of EOAD and LOAD at T0 and T1   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.

restrictedcc-by-4.0May 2024View details →

CMFeed: A Benchmark Dataset for Controllable Multimodal Feedback Synthesis

Overview 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>.  Task Uniqueness: 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. Data Collection and Annotation 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: • 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.  • 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>] • 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>]  • 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>] Filtering Process: To ensure high-quality and reliable data, the dataset underwent two levels of filtering: a) Model Agreement Filtering: Retained only comments where at least three out of the four models agreed on the sentiment. b) Probability Range Safety Margin: Comments with a sentiment probability between 0.49 and 0.51, indicating low confidence in sentiment classification, were excluded. 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. Dataset Description • Total Samples: 61,734 • Total Samples Annotated: 57,222 after filtering. • Total Posts: 3,646 • Average Likes per Post: 65.1 • Average Likes per Comment: 10.5 • Average Length of News Text: 655 words • Average Number of Images per Post: 3.7 Components of the Dataset The dataset comprises two main components: • CMFeed.csv File: Contains metadata, comment, and reaction details related to each post. • Images Folder: Contains folders with images corresponding to each post. Data Format and Fields of the CSV File The dataset is structured in CMFeed.csv file along with corresponding images in related folders. This CSV file includes the following fields: • Id: Unique identifier  • Post: The heading of the news article. • News_text: The text of the news article. • News_link: URL link to the original news article. • News_Images: A path to the folder containing images related to the post. • Post_shares: Number of times the post has been shared. • Post_reaction: A JSON object capturing reactions (like, love, etc.) to the post and their counts. • Comment: Text of the user comment. • Comment_like: Number of likes on the comment. • Comment_reaction_rank: A JSON object detailing the type and count of reactions the comment received. • Comment_link: URL link to the original comment on Facebook. • Comment_rank: Rank of the comment based on engagement and relevance. • Score: Sentiment score computed based on the consensus of sentiment analysis models. • Agreement: 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. • Sentiment_class: Categorizes the sentiment of the comment into 1 (positive) or 0 (negative). More Considerations During Dataset Construction 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: • Why not merge data from different social media platforms? 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. • Choice of four news handles: 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. • Dataset Generalizability and Bias: 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. • Dataset size and diversity: 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. Ethical considerations, data privacy and misuse prevention 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. Code and Citation • Code Repository: <a href="https://github.com/MIntelligence-Group/CMFeed/" target="_blank" rel="noopener">https://github.com/MIntelligence-Group/CMFeed/</a> • Citing the Dataset: Users of the dataset should cite the corresponding paper described at the above GitHub Repository. License & Access • This dataset is released for academic research only and is free to researchers from educational or research institutes for non-commercial purposes. • 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. Contact Please send any questions about this dataset to: • Puneet Kumar (puneet.kumar@oulu.fi), • Sarthak Malik (sarthak_m@mt.iitr.ac.in), • Balasubramanian Raman (bala@cs.iitr.ac.in), • Xiaobai Li (xiaobai.li@zju.edu.cn).

restrictedcc-by-4.0May 2024View details →

Dataset for the project "The effects of a multimodal approach for the treatment of Primary Progressive Aphasia" (GR-2018-12365105)

Dataset for the project “The effects of a multimodal approach for the treatment of Primary Progressive Aphasia” (GR-2018-12365105) Manenti Rosa (https://orcid.org/0000-0002-5955-2323); Premi Enrico (https://orcid.org/0000-0002-4342-6963  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) Specific Aim 1 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. Specific Aim 2 a) Evaluate the brain activity changes related to the intervention in targeted brain regions to establish BOLD signal fluctuations (fMRI); b) Evaluate the variations in spontaneous hemodynamic cortical activity and reactivity with a fNIRS assessment.  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. The present dataset contains the following data: •    RawData_Aim1a [Unpublished Data] 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)   •    RawData_Aim1b_ Molecular markers [Unpublished Data] o    RawData_Aim1b_ Molecular markers: an excel file containing:       - RawData_Aim1b_ Plasma_BDNF_Neurogranin: an excel sheet containing a dataset with the following variables:          Sample (Col. A);          Raw Values Plasma BDNF T0 (Col. B-D);          Raw Values Plasma BDNF T1 (Col. E-G);          Raw Values Plasma Neurogranin T0 (Col. H-J);           Raw Values Plasma Neurogranin T1 (Col. K-M).        - RawData_Aim1b_ RawData_BDNF_Simoa_Test: an excel sheet containing a dataset with the following variables:          Sample (Col. A);          Raw Simoa Values Plasma BDNF T0 (Col. B-D);           Raw Simoa Values Plasma BDNF T1 (Col. E-G). •    RawData_Aim2a_ MRI [Unpublished Data] o    RawData_Aim2a_ MRI: a folder containing:       - RawData_Aim2a_structural_MRI: a folder containing images referred on structural changes from timing T0 (baseline) to T1 (post-treatment)        - RawData_Aim2a_functional_MRI: a folder containing images referred on functional changes from timing T0 (baseline) to T1 (post-treatment)  •    RawData_Aim2b_ fNIRS [Unpublished Data] o    RawData_Aim2b_ fNIRS: a folder containing:       - 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:        Code (Col. A)        Age (Col. B)        Education (Col. C)        Gender (Col. D)        fNIRS condition at timing T0 (baseline) (Col. E)        Duration of conditions at timing T0 (baseline) (Col. F, G, H, I)        fNIRS condition at timing T1 (post-treatment) (Col. J)        Duration of conditions at timing T1 (post-treatment) (Col. K, L, M, N)        fNIRS condition at timing T2 (follow-up) (Col. O)        Duration of conditions at timing T2 (follow-up) (Col. P, Q, R, S) Preliminary Data [Published Data] Preliminary results of the project are published in: •    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 •    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:      - RawData_ Premi&Cotelli:  a dataset (excel file) containing variables referred to correlations between grey matter volume and language deficits in avPPA at timing T0  You need to satisfy these conditions in order for your request to be accepted: 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.

openAug 2023View details →

Longitudinal multimodal MRI dataset of experimental ischemic stroke in male and female rats

Preclinical MRI dataset (rat) accompanying https://doi.org/10.1177/0271678X231208993

The REgistry Study on Multimodality trEAtment foR Cerebral Hemorrhage

ClinicalTrials.gov study NCT06629532. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.

Effects of Serotonin Agonist Buspirone on Multimodal Esophageal Stimulation in Healthy Volunteers

ClinicalTrials.gov study NCT04352686. IPD Sharing: NO. Countries: 0. Publications: 0.

MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury

ClinicalTrials.gov study NCT07284771. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Multimodal Equipment for Teleophthalmology Assessment (META)

ClinicalTrials.gov study NCT05565547. IPD Sharing: NO. Countries: 0. Publications: 0.

AI-assisted Quality Control Study of Multimodal Data in the Epidemiological Survey of Shanghai Nicheng Cohort Study

ClinicalTrials.gov study NCT06961461. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Multimodal Music Therapy for Children With Anxiety Disorders

ClinicalTrials.gov study NCT01062646. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Application of Multimodal MRI-based Radiomics in Histological Grading and Prognostic Assessment of Breast Cancer

ClinicalTrials.gov study NCT07389200. IPD Sharing: NO. Countries: 0. Publications: 0.

Multimodal Database and Large Language Model for ARDS

ClinicalTrials.gov study NCT06973733. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Evaluation of Multimodal Analgesia With NOL

ClinicalTrials.gov study NCT06238830. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.

Prediction of Acute Kidney Injury After Operation of Acute Type A Aortic Dissection Based on Multimodal Model

ClinicalTrials.gov study NCT05017896. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.

Multimodal Imaging Evaluation and Prognostic Analysis of Cardiac Allograft Vasculopathy in Heart Transplantation

ClinicalTrials.gov study NCT04966988. IPD Sharing: NO. Countries: 0. Publications: 0.

Application of Multimodal MRI in Histological Grading and Prognostic Assessment of Rectal Cancer

ClinicalTrials.gov study NCT07269912. IPD Sharing: Not stated. Countries: 0. Publications: 0.