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A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1
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Drone onboard multi-modal sensor dataset for complex outdoor scenarios
<p>The Data acquisition missions were designed and executed using DJI Pilot 2’s flight route planning feature. The missions encompassed five distinct geometric patterns: 1. triangular, 2. circular, 3. rectangular, 4. linear, and 5. multi-dimensional. Each mission was configured as a waypoint flight path, allowing precise customization of parameters such as altitude, speed, and turning angle for each waypoint. The dataset consists of 3D space flight data such as take-off, landing and varying altitude to introduce the z-axis changes. It must be noted that data was logged at a frequency of 10 Hz.</p> <p>To ensure consistency within the data, identical parameters were maintained across all data acquisition missions. The dataset comprises 20 distinct flights, with each flight path repeated multiple times, resulting in approximately 30 minutes of flight time per mission. The dataset is structured as time-series data, with each flight uniquely identified by a flight number and corresponding timestamp. The drone's spatial position is represented by the variables <strong>position_x, position_y, position_z </strong>while its orientation is captured by the variables <strong>orientation_x, orientation_y, orientation_z, orientation_w</strong>. Additionally, the drone's velocity and angular velocity are represented by the variables <strong>velocity_x, velocity_y, velocity_z, angular_x, angular_y, angular_z </strong>respectively. The linear acceleration is described by the variables <strong>linear_acceleration_x, linear_acceleration_y, linear_acceleration_z</strong>. The dataset also includes environmental data such as <strong>wind_speed, wind_angle </strong>using the TriSonica Mini Wind and Weather Sensor as well as information regarding the drone's battery status, including <strong>battery_voltage, battery_current.</strong></p> <p><strong>Data Acquisition Paths: <a href="https://ucy-my.sharepoint.com/:i:/g/personal/ygrigo01_ucy_ac_cy/EYAgdcLGCWxPloO1NMnsF-8Btf390Kmx854IuDe9R3E1ig?e=3Trbuk">Data acquisition paths</a></strong></p> <p>The dataset includes labels for various operational states of the drone, such as IDLE_HOVER, ASCEND, TURN, HMSL and DESCEND. These labels can be utilized to classify the drone's current activity. Moreover, the annotated dataset can be applied in multi-task learning to predict the drone's trajectory.</p> <p>The DJI Matrice 300 RTK is utilized as the primary platform for data acquisition, leveraging its compatibility with onboard development kits to facilitate the extraction of data from its integrated sensors and flight controller. To execute the developed software the NVIDIA Jetson Xavier NX serves as the embedded computing device. Utilizing the Onboard software development kit the Jetson Xavier NX enables real-time access and processing of data from the drone's sensors and flight controller.</p>
Valence processing differs across stimulus modalities (Multi-echo)
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Drone onboard multi-modal sensor dataset
<p><strong>Drone onboard multi-modal sensor dataset :</strong></p> <p>This dataset contains timeseries data from numerous drone flights. Each flight record has a unique identifier (uid) and a timestamp indicating when the flight occurred. The drone's position is represented by the coordinates (position_x, position_y, position_z) and altitude. The orientation of the drone is represented by the quaternion (orientation_x, orientation_y, orientation_z, orientation_w). The drone's velocity and angular velocity are represented by (velocity_x, velocity_y, velocity_z) and (angular_x, angular_y, angular_z) respectively. The linear acceleration of the drone is represented by (linear_acceleration_x, linear_acceleration_y, linear_acceleration_z).</p> <p>In addition to the above, the dataset also contains information about the battery voltage (battery_voltage) and current (battery_current) and the payload attached. The payload information indicates if the drone operated with an embdded device attached (nvidia jetson), various sensors, and a solid-state weather station (trisonica).</p> <p>The dataset also includes annotations for the current state of the drone, including IDLE_HOVER, ASCEND, TURN, HMSL and DESCEND. These states can be used for classification to identify the current state of the drone. Furthermore, the labeled dataset can be used for predicting the trajectory of the drone using multi-task learning.</p> <p>For the annotation, we look at the change in position_x, position_y, position_z and yaw. Specifically, if the position_x,<br> position_y changes, it means that the drone moves in a horizontal straight line, if the position_z changes, it means that the drone performs ascending or descending (depends on whether it increases or decreases), if the yaw changes, it means that the drone performs a turn and finally if any of the above features do not change, it means the drone is in idle or hover mode.</p> <p>In addition to the features already mentioned, this dataset also includes data from various sensors including a weather station and an Inertial Measurement Unit (IMU). The weather station provides information about the weather conditions during the flight. This information includes, wind speed, and wind angle. These weather variables could be important factors that could influence the flight of the drone and battery consumption. The IMU is a sensor that measures the drone's acceleration, angular velocity, and magnetic field. The accelerometer provides information about the drone's linear acceleration, while the gyroscope provides information about the drone's angular velocity. The magnetometer measures the Earth's magnetic field, which can be used to determine the drone's orientation.</p> <p>Field deployments were performed in order to collect empirical data using a specific type of drone, specifically a DJI Matrice 300 (M300). The M300 is equipped with advanced sensors and flight control systems, which can provide high-precision flight data. The flights were designed to cover a range of flight patterns, which include triangular flight patterns, square flight patterns, polygonal flight pattern, and random flight patterns. These flight patterns were chosen to represent a variety of different flight scenarios that could be encountered in real-world applications. The triangular flight pattern consists of the drone flying in a triangular path with a fixed altitude. The square flight pattern involves the drone flying in a square path with a fixed altitude. The polygonal flight pattern consists of the drone flying in a polygonal path with a fixed altitude, and the random flight pattern involves the drone flying in a random path with a fixed altitude. Overall, this dataset contains a rich set of flight data that can be used for various research purposes, including developing and testing algorithms for drone control, trajectory planning, and machine learning.</p> <p> </p>
THÖR-Magni (Demo Subset): a new multi-modal context-rich dataset of human-robot motion
<p>The Magni Human Motion Dataset provides high-quality tracking information from motion capture, eye-gaze trackers, and on-board robot sensors in a semantically rich environment. To induce natural behavior of recorded participants, we utilized loosely scripted task assignment, which induced participants to navigate through a dynamic laboratory environment in a natural and purposeful way. The dataset sets a high-quality standard as realistic and accurate data is enhanced with semantic information, enabling development of new algorithms that rely not only on tracking information but also on contextual cues of moving agents, static and dynamic environments.</p> <p> </p> <p>Link to dashboard that uses the data: https://magni-dash.streamlit.app/</p> <p><br> Here we publish a subset of the final dataset, to accompany the presentation at the 2023 IEEE International Conference on Robotics and Automation (ICRA)</p>
Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
<p><strong>Abstract</strong></p> <p>Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.</p> <p><strong>Data Description</strong></p> <p>This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. </p> <p>As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.</p> <p>Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.</p> <p><strong>Paper & Code</strong></p> <p>The original paper for this article can be accessed here:</p> <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> <p>To access the codes relevant for this project, please access the project GitHub Repos:</p> <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> <p>If using this work, please cite it based on the above paper, or using the following BibTex:</p> <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <p> </p> <p><strong>Data Access</strong></p> <p>The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi</p> <p><strong>Funding</strong></p> <p>ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).</p>
Modal shift in North Atlantic seasonality during the last deglaciation
<p>Raw data for Brummer et al. (2020) published in Climate of the Past (https://doi.org/10.5194/cp-16-1-2020)</p>
Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft
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The open D1NAMO dataset: A multi-modal dataset for research on non-invasive type 1 diabetes management
<p>The description of the dataset is available at <a href="https://doi.org/10.1016/j.imu.2018.09.003">https://doi.org/10.1016/j.imu.2018.09.003</a></p> <p>The usage of wearable devices has gained popularity in the latest years, especially for health-care and well being. Recently there has been an increasing interest in using these devices to improve the management of chronic diseases such as diabetes. The quality of data acquired through <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/wearable-sensor">wearable sensors</a> is generally lower than what medical-grade devices provide, and existing datasets have mainly been acquired in highly controlled clinical conditions. In the context of the <em>D1NAMO</em> project — aiming to detect <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/glycemic">glycemic</a> events through non-invasive <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/ecg-abnormality">ECG pattern</a> analysis — we elaborated a dataset that can be used to help developing health-care systems based on wearable devices in non-clinical conditions. This paper describes this dataset, which was acquired on 20 healthy subjects and 9 patients with type-1 diabetes. The acquisition has been made in real-life conditions with the <em>Zephyr BioHarness 3</em> wearable device. The dataset consists of <em>ECG</em>, <em>breathing</em>, and <em><a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/accelerometer">accelerometer</a></em> signals, as well as <em>glucose</em> measurements and annotated <em>food pictures</em>. We open this dataset to the scientific community in order to allow the development and evaluation of diabetes management algorithms.</p>
Lamellar and Bi-Modal Ti-64 Microstructure Images
<p>A collection of 40 optical micrographs of Titanium-64, taken from different regions of a forged billet. The images comprise two morphologies, bi-modal and lamellar. The first 20 images (Ti64_0.jpg - Ti64_19.jpg) correspond to the bi-modal morphology. The remaning 20 images (Ti64_20.jpg - Ti64_39.jpg) corrrespond to lamellar.</p> <p>This dataset was comprised to test machine learning algorithms for classification of microstructures.</p>
Sample Multi-Modal BIDS dataset (v2.1)
<p>This a sample BIDS dataset created for continous integration of the Connectome Mapper 3.</p> <p>This dataset was acquired at the Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, using a 3T Siemens Prisma MRI scanner.</p> <p>It adopts the sub-/ses- structure and contains one T1w anatomical MRI (MPRAGE), one diffusion MRI (DSI) , and one resting-state functional MRI as well as additional Freesurfer derivatives.</p> <p>It is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. (See https://creativecommons.org/licenses/by/4.0/ for more details)</p> <p><strong><em>Changes</em></strong></p> <p><em>Version 2.1</em></p> <ul> <li>Fix issues with the resampling of the DWI and rfMRI scans with Slicer. They were regenerated in version 2.1 with `mri_convert` to better handle the 4th dimension.</li> <li>For the sake of the size of the dataset, only 100 frames in the fMRI recording has been kept and the <em>sourcedata/</em> folder has been dropped but can be easily be retrieved in the previous 2.0 version (https://zenodo.org/record/5788803#.Yb2-giYo8bV).</li> </ul> <p>Version 2.0</p> <ul> <li>For testing purposes, scans found in the root <em>sub-01</em> directory have been downsampled to 2x2x2 mm3 (MPRAGE), and to 3x3x3 mm3 (DSI and rfMRI) with the ResampleScalarVolume module of Slicer 4.6.2. A copy of the output produced in the terminal by Slicer has been created in the `code/` directory.</li> <li>Original data have been placed in <em>sourcedata/</em> in concordance to BIDS.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Data for Scalable parametric encoding of multiple modalities
<p>Two folders with figures created using Generative Encoding, using data from Stenbeck et al [1]:</p> <p> </p> <p>Folder 1 - epithelial_immune_transition</p> <p>This contains in-silico perturbations of epithelial and immune genes. Using generative encoders, the transformation begins with a histology to gene expression transformation, a perturbation of the gene expression vector, followed by an inverse transformation back to histology.</p> <p> </p> <p>Folder 2 - heatmap_he_et_al</p> <p>This contains in-silico transformation of histology tissue to gene expression. It is similar to the first folder, yet is only a single transformation (without an inverse) from histology to gene expression.</p> <p> </p> <p> </p> <p>[1] Stenbeck, Linnea; Bergenstråhle, Ludvig; Lundeberg, Joakim; Borg, Åke (2021), “Human breast cancer in situ capturing transcriptomics”, Mendeley Data, V5, doi: 10.17632/29ntw7sh4r.5</p> <p> </p>
M2 internal tide modal energy terms from a global HYCOM simulation
<p>This data set contains modal energy terms from a forward global HYCOM simulation (22.1) with realistic tide and atmospheric forcing as discussed in <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a> (On the interplay between horizontal resolution and wave drag and their effect on tidal baroclinic mode waves in realistic global ocean simulations, 2020, MC Buijsman, GR Stephenson, JK Ansong, BK Arbic, JAM Green, ... Ocean Modelling 152, 101656). <strong>Please cite this article when using these data. </strong></p> <p>This is a 4-km simulation with 41 layers. All data is on the native tripole grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>The data set contains</p> <ol> <li>The time-mean and depth-integrated M2 mode 1-5 energy terms: x (eastward) and y (northward) fluxes, KE, APE, conversion, flux divergence, and the intermodal energy conversion (topographic mode coupling) term. The terms are computed for a two-week time series starting on GMT 01-Sep-2016 01:00:00. For details see the paper.</li> <li>Positive seafloor depth, and latitude and longitude coordinates</li> </ol> <p>The M2 mode-1 SSH of the same simulation can be found here: https://doi.org/10.5281/zenodo.5514226</p> <p><a href="https://sites.google.com/site/maartenbuijsman/">https://sites.google.com/site/maartenbuijsman/</a></p>
LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild
<p><strong>LifeSnaps Dataset Documentation</strong></p> <blockquote> <p>Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the <strong>LifeSnaps dataset</strong>, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the <a href="https://rais-itn.eu/">European H2020 RAIS project</a>. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.</p> </blockquote> <p> </p> <p>The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.</p> <p><strong>Data Import: Reading CSV</strong></p> <p>For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">pandas.read_csv()</a> command.</p> <p><strong>Data Import: Setting up a MongoDB (Recommended)</strong></p> <p>To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.</p> <p>To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from <a href="https://www.mongodb.com/try/download/database-tools">here</a>.</p> <p>For the Fitbit data, run the following:</p> <pre><code>mongorestore --host localhost:27017 -d rais_anonymized -c fitbit <path to file fitbit.bson></code></pre> <p>For the SEMA data, run the following:</p> <pre><code>mongorestore --host localhost:27017 -d rais_anonymized -c sema <path to file sema.bson></code></pre> <p>For surveys data, run the following:</p> <pre><code>mongorestore --host localhost:27017 -d rais_anonymized -c surveys <path to file surveys.bson></code></pre> <p>If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.</p> <p><strong>Data Availability</strong></p> <p>The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:</p> <pre><code>{ _id: <ObjectId> id (or user_id): <ObjectId> type: <String> data: <Object> }</code></pre> <p>Each document consists of four fields: id (also found as user_id in sema and survey collections), type, and data. The _id field is the MongoDB-defined primary key and can be ignored. The id field refers to a user-specific ID used to uniquely identify each user across all collections. The type field refers to the specific data type within the collection, e.g., steps, heart rate, calories, etc. The data field contains the actual information about the document e.g., steps count for a specific timestamp for the steps type, in the form of an embedded object. The contents of the data object are type-dependent, meaning that the fields within the data object are different between different types of data. As mentioned previously, all times are stored in local time, and user IDs are common across different collections. For more information on the available data types, see the related publication.</p> <p><strong>Surveys Encoding</strong></p> <p><strong>BREQ2</strong></p> <p><em>Why do you engage in exercise?</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>engage[SQ001]</td> <td>I exercise because other people say I should</td> </tr> <tr> <td>engage[SQ002]</td> <td>I feel guilty when I don’t exercise</td> </tr> <tr> <td>engage[SQ003]</td> <td>I value the benefits of exercise</td> </tr> <tr> <td>engage[SQ004]</td> <td>I exercise because it’s fun</td> </tr> <tr> <td>engage[SQ005]</td> <td>I don’t see why I should have to exercise</td> </tr> <tr> <td>engage[SQ006]</td> <td>I take part in exercise because my friends/family/partner say I should</td> </tr> <tr> <td>engage[SQ007]</td> <td>I feel ashamed when I miss an exercise session</td> </tr> <tr> <td>engage[SQ008]</td> <td>It’s important to me to exercise regularly</td> </tr> <tr> <td>engage[SQ009]</td> <td>I can’t see why I should bother exercising</td> </tr> <tr> <td>engage[SQ010]</td> <td>I enjoy my exercise sessions</td> </tr> <tr> <td>engage[SQ011]</td> <td>I exercise because others will not be pleased with me if I don’t</td> </tr> <tr> <td>engage[SQ012]</td> <td>I don’t see the point in exercising</td> </tr> <tr> <td>engage[SQ013]</td> <td>I feel like a failure when I haven’t exercised in a while</td> </tr> <tr> <td>engage[SQ014]</td> <td>I think it is important to make the effort to exercise regularly</td> </tr> <tr> <td>engage[SQ015]</td> <td>I find exercise a pleasurable activity</td> </tr> <tr> <td>engage[SQ016]</td> <td>I feel under pressure from my friends/family to exercise</td> </tr> <tr> <td>engage[SQ017]</td> <td>I get restless if I don’t exercise regularly</td> </tr> <tr> <td>engage[SQ018]</td> <td>I get pleasure and satisfaction from participating in exercise</td> </tr> <tr> <td>engage[SQ019]</td> <td>I think exercising is a waste of time</td> </tr> </tbody> </table> <p><strong>PANAS</strong></p> <p><em>Indicate the extent you have felt this way over the past week </em></p> <table> <tbody> <tr> <td>P1[SQ001]</td> <td>Interested</td> </tr> <tr> <td>P1[SQ002]</td> <td>Distressed</td> </tr> <tr> <td>P1[SQ003]</td> <td>Excited</td> </tr> <tr> <td>P1[SQ004]</td> <td>Upset</td> </tr> <tr> <td>P1[SQ005]</td> <td>Strong</td> </tr> <tr> <td>P1[SQ006]</td> <td>Guilty</td> </tr> <tr> <td>P1[SQ007]</td> <td>Scared</td> </tr> <tr> <td>P1[SQ008]</td> <td>Hostile</td> </tr> <tr> <td>P1[SQ009]</td> <td>Enthusiastic</td> </tr> <tr> <td>P1[SQ010]</td> <td>Proud</td> </tr> <tr> <td>P1[SQ011]</td> <td>Irritable</td> </tr> <tr> <td>P1[SQ012]</td> <td>Alert</td> </tr> <tr> <td>P1[SQ013]</td> <td>Ashamed</td> </tr> <tr> <td>P1[SQ014]</td> <td>Inspired</td> </tr> <tr> <td>P1[SQ015]</td> <td>Nervous</td> </tr> <tr> <td>P1[SQ016]</td> <td>Determined</td> </tr> <tr> <td>P1[SQ017]</td> <td>Attentive</td> </tr> <tr> <td>P1[SQ018]</td> <td>Jittery</td> </tr> <tr> <td>P1[SQ019]</td> <td>Active</td> </tr> <tr> <td>P1[SQ020]</td> <td>Afraid</td> </tr> </tbody> </table> <p><strong>Personality</strong></p> <p><em>How Accurately Can You Describe Yourself?</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>ipip[SQ001]</td> <td>Am the life of the party.</td> </tr> <tr> <td>ipip[SQ002]</td> <td>Feel little concern for others.</td> </tr> <tr> <td>ipip[SQ003]</td> <td>Am always prepared.</td> </tr> <tr> <td>ipip[SQ004]</td> <td>Get stressed out easily.</td> </tr> <tr> <td>ipip[SQ005]</td> <td>Have a rich vocabulary.</td> </tr> <tr> <td>ipip[SQ006]</td> <td>Don't talk a lot.</td> </tr> <tr> <td>ipip[SQ007]</td> <td>Am interested in people.</td> </tr> <tr> <td>ipip[SQ008]</td> <td>Leave my belongings around.</td> </tr> <tr> <td>ipip[SQ009]</td> <td>Am relaxed most of the time.</td> </tr> <tr> <td>ipip[SQ010]</td> <td>Have difficulty understanding abstract ideas.</td> </tr> <tr> <td>ipip[SQ011]</td> <td>Feel comfortable around people.</td> </tr> <tr> <td>ipip[SQ012]</td> <td>Insult people.</td> </tr> <tr> <td>ipip[SQ013]</td> <td>Pay attention to details.</td> </tr> <tr> <td>ipip[SQ014]</td> <td>Worry about things.</td> </tr> <tr> <td>ipip[SQ015]</td> <td>Have a vivid imagination.</td> </tr> <tr> <td>ipip[SQ016]</td> <td>Keep in the background.</td> </tr> <tr> <td>ipip[SQ017]</td> <td>Sympathize with others' feelings.</td> </tr> <tr> <td>ipip[SQ018]</td> <td>Make a mess of things.</td> </tr> <tr> <td>ipip[SQ019]</td> <td>Seldom feel blue.</td> </tr> <tr> <td>ipip[SQ020]</td> <td>Am not interested in abstract ideas.</td> </tr> <tr> <td>ipip[SQ021]</td> <td>Start conversations.</td> </tr> <tr> <td>ipip[SQ022]</td> <td>Am not interested in other people's problems.</td> </tr> <tr> <td>ipip[SQ023]</td> <td>Get chores done right away.</td> </tr> <tr> <td>ipip[SQ024]</td> <td>Am easily disturbed.</td> </tr> <tr> <td>ipip[SQ025]</td> <td>Have excellent ideas.</td> </tr> <tr> <td>ipip[SQ026]</td> <td>Have little to say.</td> </tr> <tr> <td>ipip[SQ027]</td> <td>Have a soft heart.</td> </tr> <tr> <td>ipip[SQ028]</td> <td>Often forget to put things back in their proper place.</td> </tr> <tr> <td>ipip[SQ029]</td> <td>Get upset easily.</td> </tr> <tr> <td>ipip[SQ030]</td> <td>Do not have a good imagination.</td> </tr> <tr> <td>ipip[SQ031]</td> <td>Talk to a lot of different people at parties.</td> </tr> <tr> <td>ipip[SQ032]</td> <td>Am not really interested in others.</td> </tr> <tr> <td>ipip[SQ033]</td> <td>Like order.</td> </tr> <tr> <td>ipip[SQ034]</td> <td>Change my mood a lot.</td> </tr> <tr> <td>ipip[SQ035]</td> <td>Am quick to understand things.</td> </tr> <tr> <td>ipip[SQ036]</td> <td>Don't like to draw attention to myself.</td> </tr> <tr> <td>ipip[SQ037]</td> <td>Take time out for others.</td> </tr> <tr> <td>ipip[SQ038]</td> <td>Shirk my duties.</td> </tr> <tr> <td>ipip[SQ039]</td> <td>Have frequent mood swings.</td> </tr> <tr> <td>ipip[SQ040]</td> <td>Use difficult words.</td> </tr> <tr> <td>ipip[SQ041]</td> <td>Don't mind being the centre of attention.</td> </tr> <tr> <td>ipip[SQ042]</td> <td>Feel others' emotions.</td> </tr> <tr> <td>ipip[SQ043]</td> <td>Follow a schedule.</td> </tr> <tr> <td>ipip[SQ044]</td> <td>Get irritated easily.</td> </tr> <tr> <td>ipip[SQ045]</td> <td>Spend time reflecting on things.</td> </tr> <tr> <td>ipip[SQ046]</td> <td>Am quiet around strangers.</td> </tr> <tr> <td>ipip[SQ047]</td> <td>Make people feel at ease.</td> </tr> <tr> <td>ipip[SQ048]</td> <td>Am exacting in my work.</td> </tr> <tr> <td>ipip[SQ049]</td> <td>Often feel blue.</td> </tr> <tr> <td>ipip[SQ050]</td> <td>Am full of ideas.</td> </tr> </tbody> </table> <p><strong>STAI</strong></p> <p><em>Indicate how you feel right now</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>STAI[SQ001]</td> <td>I feel calm</td> </tr> <tr> <td>STAI[SQ002]</td> <td>I feel secure</td> </tr> <tr> <td>STAI[SQ003]</td> <td>I am tense</td> </tr> <tr> <td>STAI[SQ004]</td> <td>I feel strained</td> </tr> <tr> <td>STAI[SQ005]</td> <td>I feel at ease</td> </tr> <tr> <td>STAI[SQ006]</td> <td>I feel upset</td> </tr> <tr> <td>STAI[SQ007]</td> <td>I am presently worrying over possible misfortunes</td> </tr> <tr> <td>STAI[SQ008]</td> <td>I feel satisfied</td> </tr> <tr> <td>STAI[SQ009]</td> <td>I feel frightened</td> </tr> <tr> <td>STAI[SQ010]</td> <td>I feel comfortable</td> </tr> <tr> <td>STAI[SQ011]</td> <td>I feel self-confident</td> </tr> <tr> <td>STAI[SQ012]</td> <td>I feel nervous</td> </tr> <tr> <td>STAI[SQ013]</td> <td>I am jittery</td> </tr> <tr> <td>STAI[SQ014]</td> <td>I feel indecisive</td> </tr> <tr> <td>STAI[SQ015]</td> <td>I am relaxed</td> </tr> <tr> <td>STAI[SQ016]</td> <td>I feel content</td> </tr> <tr> <td>STAI[SQ017]</td> <td>I am worried</td> </tr> <tr> <td>STAI[SQ018]</td> <td>I feel confused</td> </tr> <tr> <td>STAI[SQ019]</td> <td>I feel steady</td> </tr> <tr> <td>STAI[SQ020]</td> <td>I feel pleasant</td> </tr> </tbody> </table> <p><strong>TTM</strong></p> <p><em>Do you engage in regular physical activity according to the definition above? How frequently did each event or experience occur in the past month?</em></p> <table> <tbody> <tr> <td>Code</td> <td>Text</td> </tr> <tr> <td>processes[SQ002]</td> <td>I read articles to learn more about physical activity.</td> </tr> <tr> <td>processes[SQ003]</td> <td>I get upset when I see people who would benefit from physical activity but choose not to do physical activity.</td> </tr> <tr> <td>processes[SQ004]</td> <td>I realize that if I don't do physical activity regularly, I may get ill and be a burden to others.</td> </tr> <tr> <td>processes[SQ005]</td> <td>I feel more confident when I do physical activity regularly.</td> </tr> <tr> <td>processes[SQ006]</td> <td>I have noticed that many people know that physical activity is good for them.</td> </tr> <tr> <td>processes[SQ007]</td> <td>When I feel tired, I make myself do physical activity anyway because I know I will feel better afterwards.</td> </tr> <tr> <td>processes[SQ008]</td> <td>I have a friend who encourages me to do physical activity when I don't feel up to it.</td> </tr> <tr> <td>processes[SQ009]</td> <td>One of the rewards of regular physical activity is that it improves my mood.</td> </tr> <tr> <td>processes[SQ010]</td> <td>I tell myself that I can keep doing physically activity if I try hard enough.</td> </tr> <tr> <td>processes[SQ011]</td> <td>I keep a set of physical activity clothes with me so I can do physical activity whenever I get the time.</td> </tr> <tr> <td>processes[SQ012]</td> <td>I look for information related to physical activity.</td> </tr> <tr> <td>processes[SQ013]</td> <td>I am afraid of the results to my health if I do not do physical activity.</td> </tr> <tr> <td>processes[SQ014]</td> <td>I think that by doing regular physical activity I will not be a burden to the healthcare system.</td> </tr> <tr> <td>processes[SQ015]</td> <td>I believe that regular physical activity will make me a healthier, happier person.</td> </tr> <tr> <td>processes[SQ016]</td> <td>I am aware of more and more people who are making physical activity a part of their lives.</td> </tr> <tr> <td>processes[SQ017]</td> <td>Instead of taking a nap after work, I do physical activity.</td> </tr> <tr> <td>processes[SQ018]</td> <td>I have someone who encourages me to do physical activity. </td> </tr> <tr> <td>processes[SQ019]</td> <td>I try to think of physical activity as a time to clear my mind as well as a workout for my body.</td> </tr> <tr> <td>processes[SQ020]</td> <td>I make commitments to do physical activity.</td> </tr> <tr> <td>processes[SQ021]</td> <td>I use my calendar to schedule my physical activity time.</td> </tr> <tr> <td>processes[SQ022]</td> <td>I find out about new methods of being physically active.</td> </tr> <tr> <td>processes[SQ023]</td> <td>I get upset when I realize that people I love would have better health if they were physically active.</td> </tr> <tr> <td>processes[SQ024]</td> <td>I think that regular physical activity plays a role in reducing health care costs.</td> </tr> <tr> <td>processes[SQ025]</td> <td>I feel better about myself when I do physical activity.</td> </tr> <tr> <td>processes[SQ026]</td> <td>I notice that famous people often say that they do physical activity regularly.</td> </tr> <tr> <td>processes[SQ027]</td> <td>Instead of relaxing by watching TV or eating, I take a walk or am physically active.</td> </tr> <tr> <td>processes[SQ028]</td> <td>My friends encourage me to do physical activity.</td> </tr> <tr> <td>processes[SQ029]</td> <td>If I engage in regular physical activity, I find that I get the benefit of having more energy.</td> </tr> <tr> <td>processes[SQ030]</td> <td>I believe that I can do physical activity regularly. </td> </tr> <tr> <td>processes[SQ031]</td> <td>I make sure I always have a clean set of physical activity clothes.</td> </tr> </tbody> </table> <p><strong>Files Description</strong></p> <p><em>Scored Surveys:</em> CSV files containing scored versions of BREQ-2, IPIP, PANAS, STAI, and TTM surveys in scored_surveys folder</p> <p><em>Fitbit & EMA Data (daily granularity):</em> csv_rais_anonymized/daily_fitbit_sema_df_unprocessed.csv</p> <p><em>Fitbit & EMA Data (hourly granularity):</em> csv_rais_anonymized/hourly_fitbit_sema_df_unprocessed.csv</p> <p><em>MongoDB Dumps (compressed for practicality):</em> mongo_rais_anonymized/fitbit.bson for Fitbit data, mongo_rais_anonymized/sema for EMA data, and mongo_rais_anonymized/surveys for raw surveys data. </p> <p><strong>Code Availability</strong></p> <p><em>Data Anonymization:</em> <a href="https://github.com/syfantid/RAIS-Anonymization">https://github.com/syfantid/RAIS-Anonymization</a></p> <p><em>Exploratory Data Analysis:</em> <a href="https://github.com/kcristinaa/LifeSnaps-EDA">https://github.com/kcristinaa/LifeSnaps-EDA</a></p> <p><strong>Related Publications</strong></p> <p>Sofia Yfantidou, Christina Karagianni, Stefanos Efstathiou, Athena Vakali, Joao Palotti, Dimitrios Panteleimon Giakatos, Thomas Marchioro, Andrei Kazlouski, Elena Ferrari, and Sarunas Girdzijauskas, 2022, LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild (Submitted for peer-review).</p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813162. The content of this paper reflects only the authors' view and the Agency and the Commission are not responsible for any use that may be made of the information it contains. First and foremost, the authors would like to thank the participants of the LifeSnaps study who agreed to share their data for scientific advancement. The authors would like to further thank the web developers, T. Valk and S. Karamanidis, for their contribution to the project, all past and present RAIS fellows for their help with participants' recruitment, G. Pallis and M. Christodoulaki for their support with the ethics committee application, and B. Carminati for her feedback on data anonymization and privacy considerations.</p>
AdsMT: Multi-modal Transformer for Predicting Global Minimum Adsorption Energy
<p>We built three Global Minimum Adsorption Energy (GMAE) benchmark datasets named OCD-GMAE, Alloy-GMAE and FG-GMAE from OC20-Dense, Catalysis Hub, and `functional groups' (FG)-dataset datasets through strict data cleaning, and each data point represents a unique combination of catalyst surface and adsorbate. These new benchmark datasets can be beneficial for future ML study on GMAE prediction.</p> <p>In addition, a similar data cleaning procedure was employed on the OC20 dataset to create a new dataset named OC20-LMAE, which comprises surface/adsorbate pairings along with their local minimum adsorption energies (LMAE). The OC20-LMAE dataset contains 363,937 data points and serves as an effective resource for model pretraining.</p>
Sensor-based Pallet Activity Recognition in Logistics (SPARL Version 2) - A multi-modal Dataset
<p>SPARL is a freely accessible data set for sensor-based activity recognition of pallets in logistics. The data set consists of 20 recordings from three scenarios. A description of the scenarios can be found in the protocol file.</p> <p>Four different sensors were used simultaneously for all recordings:</p> <ul> <li>MSR Electronics MSR 145 <ul> <li>Sampling rate 50 Hz</li> </ul> </li> <li>MBIENTLAB MetaMotionS <ul> <li>Sampling rate 100 Hz</li> </ul> </li> <li>Kistler KiDaQ Module 5512A <ul> <li>Sampling rate 100 kHz</li> <li>the raw data is also downsampled to 5 kHz and 20 kHz for easier processing </li> </ul> </li> <li>Holybro Flightcontroller PX4FMU <ul> <li>The board uses two accelerometers and two gyroscopes, all with a sampling rate of 1000 Hz <ul> <li>Accelerometer 1: IvenSense MPU6000 </li> <li>Accelerometer 2: STMicroelectronics LSM303D </li> <li>Gyroscope 1: IvenSense MPU6000</li> <li>Gyroscope 2: STMicroelectronics L3GD20</li> </ul> </li> </ul> </li> </ul> <p>The recordings were accompanied by three logitech Mevo Start cameras, of which all recordings are included anonymously in the data set. </p> <p>The videos were annotated by one person in each frame. For this purpose, the annotation tool SARA was used, which can be found <a href="../records/8189341">here</a>. The JSON schema used for annotation is also included in the SPARL dataset. The R code used our evaluation can be found in <a title="https://github.com/bommert/WGTL24" href="https://github.com/bommert/WGTL24">GitHub</a>.</p> <p>If you have any questions about the dataset, please contact: sven.franke@tu-dortmund.de</p> <p><strong>If you use this dataset for research, please cite the following paper: “Data-driven, sensor-based taxonomy for environmental life cycle assessment of pallets”, Nr. 20 (2024): Logistics Journal: Proceedings, DOI: <a href="http://dx.doi.org/10.2195/lj_proc_franke_en_202410_01" target="_blank" rel="noopener">10.2195/lj_proc_franke_en_202410_01</a></strong></p>
REHAB24-6: A multi-modal dataset of physical rehabilitation exercises
<p>To enable the evaluation of HPE models and the development of exercise feedback systems, we produced a new rehabilitation dataset (REHAB24-6). The main focus is on a diverse range of exercises, views, body heights, lighting conditions, and exercise mistakes. With the publicly available RGB videos, skeleton sequences, repetition segmentation, and exercise correctness labels, this dataset offers the most comprehensive testbed for exercise-correctness-related tasks.</p> <h2>Contents</h2> <ul> <li>65 recordings (184,825 frames, 30 FPS): <ul> <li>RGB videos from two cameras (<code>videos.zip</code>, horizontal = Camera17, vertical = Camera18);</li> <li>3D and 2D projected positions of 41 motion capture marker (<code><2/3>d_markers.zip</code>, marker labels in <code>marker_names.txt</code>);</li> <li>3D and 2D projected positions of 26 skeleton joints (<code><2/3>d_joints.zip</code>, joint labels in <code>joint_names.txt</code>);</li> </ul> </li> <li>Annotation of 1,072 exercise repetitions (<code>Segmentation.csv</code>, indexed based <strong>only on</strong> 30 FPS data, described in <code>Segmentation.txt</code>): <ul> <li>Temporal segmentation (start/end frame, most between 2–5 seconds);</li> <li>Binary correctness label (around 90 from each category in each exercise, except Ex3 with around 50);</li> <li>Exercise direction (around 90 from each direction in each exercise);</li> <li>Lighting conditions label.</li> </ul> </li> </ul> <h2>Recording Conditions</h2> <p>Our laboratory setup included 18 synchronized sensors (2 RGB video cameras, 16 ultra-wide motion capture cameras) spread around an 8.2 × 7 m room. The RGB cameras were located in the corners of the room, one in a horizontal position (hor.), providing a larger field of view (FoV), and one in a vertical (ver.), resulting in a narrower FoV. Both types of cameras were synchronized with a sampling frequency of 30 frames per second (FPS).</p> <p>The subjects wore motion capture body suits with 41 markers attached to them, which were detected by optical cameras. The OptiTrack Motive 2.3.0 software inferred the 3D positions of the markers in virtual centimeters and converted them into a skeleton with 26 joints, forming our human pose 3D ground truth (GT).</p> <p>To acquire a 2D version of the ground truth in pixel coordinates, we applied a projection of the virtual coordinates into the camera using the simplified pinhole model. We estimated the parameters for this projection as follows. First, the virtual position of the cameras was estimated using measuring tape and knowledge of the virtual origin. Then, the orientation of the cameras was optimized by matching the virtual marker positions with their position in the videos.</p> <p>We also simulated changes in lighting conditions: a few videos were shot in the natural evening light, which resulted in worse visibility, while the rest were under artificial lighting.</p> <h2>Exercises</h2> <p>10 subjects participated in our recording and consented to release the data publicly: 6 males and 4 females of different ages (from 25 to 50) and fitness levels. A physiotherapist instructed the subjects on how to perform the exercises so that at least five repetitions were done in what he deemed the correct way and five more incorrectly. The participants had a certain degree of freedom, e.g., in which leg they used in Ex4 and Ex5. Similarly, the physiotherapist suggested different exercise mistakes for each subject.</p> <ul> <li><strong>Ex1 = Arm abduction</strong>: sideway raising of the straightened right arm;</li> <li><strong>Ex2 = Arm VW</strong>: fluent transition of arms between V (arms straight up) and W (elbows down, hands up) shape;</li> <li><strong>Ex3 = Push-ups</strong>: push-ups with hands on a table;</li> <li><strong>Ex4 = Leg abduction</strong>: sideway raising of the straightened leg;</li> <li><strong>Ex5 = Leg lunge</strong>: pushing a knee of the back leg down while keeping a right angle on the front knee;</li> <li><strong>Ex6 = Squats</strong>.</li> </ul> <p>Every exercise was also executed in two directions, resulting in different views of the subject depending on the camera. Facing the horizontal camera resulted in a front view for that camera and a profile from the other. Facing the wall between the cameras shows the subject from half-profile in both cameras. A rare direction, only used for push-ups due to the use of the table, was facing the vertical camera, with the views being reversed compared to the first orientation.</p> <h2>Citation</h2> <p>Cite the related conference paper:</p> <p>Černek, A., Sedmidubsky, J., Budikova P.: REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods. 17th International Conference on Similarity Search and Applications (SISAP). Springer, 14 pages, 2024.</p> <h2>License</h2> <p>This dataset is for academic or non-profit organization noncomercial research use only. By using you agree to appropriately reference the paper above in any publication making of its use. For comercial purposes contact us at info@visioncraft.ai</p>
A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys
<p>A multi-type geobody dataset for training SAG model, including channel, paloekarst, salt body, and so on.</p> <p>A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (<a href="https://arxiv.org/abs/2409.04962">[2409.04962] A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (arxiv.org)</a>)</p> <p> </p>
Additional material for "Sharp Congruences Adequate with Temporal Logics Combining Weak and Strong Modalities"
<p>Complementary material including the proofs of all theorems presented at TACAS 2020, a detailed description of the RERS 2019 experiment, and the actual data used for the RERS 2018,RERS 2019, and TFTP experiments.</p>
Alternation emerges as a multi-modal strategy for turbulent odor navigation - Dataset
<p>Dataset from 3D direct numerical simulation of odor evolution in a turbulent channel flow.</p> <p>The dataset contains two .mat files with nose (z ~ 50 cm) and ground (z = 0) level 2D slices;</p> <p>coordinates.mat with the coordinates in X and Y directions;</p> <p>and a Jupyter Notebook file (Read_nose_ground_dataset) to read and plot the 2D fields.<br><br>Updated on May 20th 2025<br>3D velocity data have been added to the dataset.</p>
ScienceDex guides
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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.