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1,389 results for “Multimodal”
Dataset and Data analysis "Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?"
<p>Data Analysis for the publication 10.1002/jbio.202000264.</p> <p>It is divided in three different folders describing three different part of the data analysis:</p> <p><strong>A. DATA TREATMENT RAMAN (Folder 1)</strong></p> <p><em>1. Import data using the Import_Raman script.<br> 2. Plot Spectra and integrate DOX band<br> Figure 1A<br> Figure 1B<br> 3. PCA<br> Figure 1D<br> Figure 1C<br> SM 1<br> 4. PLS<br> Figure 1F<br> Figure 1E</em></p> <p><strong>B. ANALYSIS OF IR DATA AND MULTIMODAL IR-RAMAN OF DOX UPTAKE (Folder 2)</strong></p> <p><em>1 Load Data IR<br> 2 Exploratory Analysis IR<br> Figure 2A<br> 3 PCA <br> SM 2<br> 4. Partial Least Squares vs time<br> Figure 2C<br> Figure 2B<br> 5. Partial Least Squares vs Raman Signal<br> Figure 2E<br> Figure 2D<br> 6. Make Averages and clean up Data for DATA Fusion<br> IR<br> Raman<br> 7. 2DCORR<br> Figure 3B<br> 8. MCR_ALS WITH DATA FUSION<br> Fitting of the concentration of Raman using the method in [9].<br> MCR-ALS<br> Figures 4 A, B and C</em></p> <p> </p> <p><strong>C. SIMULATION (Folder 3)</strong></p> <p><em>1. Load Raman DATA<br> 2. Simulate Raman DAta<br> 3. Load and simulate IR Data<br> 4. 2D corr<br> Figure 3A</em></p> <p> </p> <p>. Each folder contains a .mlx with the data analysis performed. Figures numbering corresponds to the one found in the article.</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024)
<p>The data contains simulation results from 2000-2024, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999)
<p>The data contains simulation results from 1975-1999, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671245</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974)
<p>The data contains simulation results from 1950-1974, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:<br>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Plains zebra 2019 multimodal communication dataset
<p>Multimodality is a virtually ubiquitous feature of communication. With the increasing interest in how animals, including humans, use multimodal and multicomponent signals in social interactions, there is an acute need for standardized and rigorous tools that will allow us to visualize, and analyze these signals as they occur in naturalistic interactions as a complex, integrated system. Network theory is a powerful methodology for intuitively visualizing and investigating the relationships between entities. Here, we propose a new application of network theory for analyzing multimodal communication. Using a case study of natural multimodal interactions in wild plains zebras (<em>Equus quagga</em>), we introduce the descriptive power of network metrics by providing an objective set of metrics to: (a) describe the relationships between simultaneously produced signals within and between modalities; and (b) infer signal meaning and function. Here we make available the multimodal communication data collected during our 2019 field season.</p>
ScientISST MOVE: Annotated Wearable Multimodal Biosignals recorded during Everyday Life Activities in Naturalistic Environments
<p>A multi-modality, multi-activity, and multi-subject dataset of wearable biosignals.</p><p><strong>Modalities:</strong> ECG, EMG, EDA, PPG, ACC, TEMP</p><p><strong>Main Activities:</strong> Lift object, Greet people, Gesticulate while talking, Jumping, Walking, and Running</p><p><strong>Cohort: </strong>17 subjects (10 male, 7 female); median age: 24</p><p><strong>Devices: </strong>2x ScientISST Core + 1x Empatica E4</p><p><strong>Body Locations: </strong>Chest, Abdomen, Left bicep, wrist and index finger</p><p>No filter has been applied to the signals, but the correct transfer functions were applied, so the data is given in relevant unis (mV, uS, g, ºC).</p><p>For more information on background, methods and the acquisition protocol, refer to <a href="https://doi.org/10.13026/0ppk-ha30">https://doi.org/10.13026/0ppk-ha30</a>.</p><p>========</p><p>In this repository, there are two formats available:</p><h4><strong>a) LTBio's Biosignal files. Should be open like:</strong></h4><p><i>x = Biosignal.load(path)</i></p><p>LTBio Package: <a href="https://pypi.org/project/LongTermBiosignals/">https://pypi.org/project/LongTermBiosignals/</a></p><p>Under the directory <i>biosignal</i>, the following tree structure is found: <i>subject/x.biosignal</i>, where <i>subject</i> is the subject's code, and <i>x</i> is any of the following {<i> acc_chest, acc_wrist, ecg, eda, emg, ppg, temp</i> }. Each file includes the signals recorded from every sensor that acquires the modality after which the file is named, independently of the device.</p><p>Channels, activities and time intervals can be easily indexed with the index operator <i>[]</i> ( <a href="https://ltbio.readthedocs.io/en/latest/learn/basic/ltbio101.html">https://ltbio.readthedocs.io/en/latest/learn/basic/ltbio101.html</a> ).</p><p>A sneak peak of the signals can also be quickly plotted with: <i>x.preview.plot()</i></p><p>Any Biosignal can be easily converted to NumPy arrays or DataFrames, if needed.</p><h4><strong>b) CSV files. Can be open like:</strong></h4><p><i>x = pandas.read_csv(path)</i></p><p>Pandas Package: <a href="https://pypi.org/project/pandas/">https://pypi.org/project/pandas/</a></p><p>These files can be found under the directory <i>csv</i>, named as <i>subject.csv</i>, where <i>subject</i> is the subject's code. There is only one file per subject, containing their full session and all biosignal modalities. When read as tables, the time axis is in the first column, each sensor is in one of the middle columns, and the activity labels are in the last column. In each row are the samples of each sensor, if any, at each timestamp. At any given timestamp, if there is no sample for a sensor, it means the acquisition was interrupted for that sensor, which happens between activities, and sometimes for short periods during the running activity. Also in each row, on the last column, is one or more activity labels, if an activity was taking place at that timestamp. If there are multiple annotations, the labels are separated by vertical bars (e.g '<i>run | sprint</i>'). If there are no annotations, the column is empty for that timestamp.</p><p>In order to provide a tabular format with sensors with different sampling frequencies, the sensors with sampling frequency lower than 500 Hz were upsampled to 500 Hz. This way, the tables are regularly sampled, i.e., there is a row every 2 ms. If a sensor was not acquiring at a given timestamp, the corresponding cell with be empty. So, not only the segments with samples are regularly sampled, but the interruptions are also discretised. This means that if, after an interruption, a sensor starts acquiring at a non regular timestamp, the first sample will be written on the previous or the following timestamp, by half-up rounding. Naturally, this process cumulatively introduces lags in the table, some of which cancel out. Each individual lag is no longer than half the sampling period (1 ms), hence negligible. The cumulative lags are no longer than 48 ms for all subjects, which is also negligible. Nevertheless, only the LBio's Biosignal format preserves the exact original timestamps (10E-6 precision) of all samples and the original sampling frequencies.</p><p>================</p><p>Both include annotations of the activities, however LTBio bio signal files have better time resolution and include clinical data and demographic data as well.</p>
Artificial flowers as a tool for investigating multimodal flower choice in wild insects
<p>Flowers come in a variety of colours, shapes, sizes, and odours. Flowers also differ in the quality and quantity of nutritional rewards they provide to entice potential pollinators to visit. Given this diversity, generalist flower-visiting insects face the considerable challenge of deciding which flowers to feed on and which to ignore.</p> <p>Working with real flowers poses logistical challenges due to correlations between flower traits, maintenance costs, and uncontrolled variables. Here we overcome this challenge by designing multi-modal artificial flowers that varied in visual, olfactory, and reward attributes. We used artificial flowers to investigate the impact of seven floral attributes (three visual cues, two olfactory cues, and two rewarding attributes) on flower visitation and species richness. We investigated how flower attributes influenced two phases of the decision-making process: the decision to land on a flower, and the decision to feed on a flower.</p> <p>Artificial flowers attracted 890 individual insects representing 15 morphospecies spanning seven arthropod orders. Honeybees were the most common visitors accounting for 46% of visitors. Higher visitation rates were driven by the presence of nectar, the presence of linalool, flower shape, and flower colour, and was negatively impacted by the presence of citral. Species richness was driven by the presence of nectar, the presence of linalool, and flower colour. For hymenopterans, the probability of landing the artificial flowers was influenced by the presence of nectar or pollen, shape, and the presence of citral and/or linalool. The probability of feeding increased when flowers contained nectar. For dipterans, the probability of landing on artificial flowers increased when the flower was yellow and contained linalool. The probability of feeding increased when flowers contained pollen, nectar, and linalool.</p> <p>Our results demonstrate the multi-attribute nature of flower preferences and highlight the usefulness of artificial flowers as tools for studying flower visitation in wild insects.</p>
Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset
<p><strong>Dataset description</strong></p><p>This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation">OpenDR agricultural dataset generator tool</a>.</p><p>It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.</p><p><strong>Folder configuration</strong></p><p>The dataset contains 4 folders for different lighting conditions:</p><ul><li>noon cloudy</li><li>noon stormy</li><li>dawn cloudy</li><li>dusk</li></ul><p>Each contains UAV and UGV folders. UAV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>camera: contains generated RGB images.</li><li>gps: contains the three-axis location of global positioning sensor saved in TXT files.</li><li>inertial unit: contains the inertial unit date in TXT files.</li></ul><p>UGV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>front_bottom_camera: contains generated RGB images.</li><li>Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.</li><li>imu_robotti: contains the inertial unit date in TXT files.</li><li>velodyne: contains lidar data in PCD files.</li></ul><p><strong>Data format</strong></p><p>The dataset includes</p><ul><li>The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.</li><li>The GPS measurement TXT files include the robot position in x, y, and z order.</li><li>Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.</li></ul><p><strong>File naming convention</strong></p><p>Each data is named "s_i{_segmented, _annotation}.ext", where:</p><ul><li><strong>s</strong> denotes the simulated time in seconds.</li><li><strong>i</strong> denotes the index counting every 10ms of simulated time.</li><li><strong>ext</strong> denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.</li><li>Labels <strong>_segmented</strong> and <strong>_annotation </strong>appended to the name for segmentation image and object annotations, respectively.</li></ul><p>Each segmented image uses the following RGB color mapping:</p><ul><li>Tree: 0.1, 0.4, 0.0</li><li>Apple Tree: 0.85, 0.49, 0.57</li><li>Cow: 0.380, 0.220, 0.137</li><li>Sheep: 0.937, 0.921, 0.862</li><li>Fox: 0.992, 0.376, 0.086</li><li>Barn: 0.625, 0.293, 0.226</li><li>Cat: 0.870, 0.580, 0.0</li><li>Deer: 0.415, 0.364, 0.302</li><li>Human: 1.0, 0.855, 0.672</li></ul>
3D and multimodal X-ray microscopy reveals the impact of voids in CIGS solar cells
<p>Data repository for the article "3D and multimodal X-ray microscopy reveals the impact of voids in CIGS solar cells" <strong>DOI: </strong>10.1002/advs.202301873</p>
Multimodal datataset of EEG, ECG and audio recordings of individual musicians playing emotional music
<p>This dataset contains EEG, ECG and audio recordings of 11 individual musicians playing emotional music on their instrument. The dataset consists of two parts:</p> <ol> <li> <p>Experiment: Musicians’ self-reported ratings, audio recordings, and physiological recordings where the 11 expert musicians were asked to play at least 4~2-minute unfamiliar (non-popularly known) musical pieces. Participants were asked to play at least once one of the following emotions: happiness, sadness, relaxation, and anger. For the physiological recordings EEG, ECG, and GSR signals were recorded. Each musican’s data is denoted by MS_ followed by the order in which they were recorded.</p> </li> <li> <p>Self-report questionnaire: A self-assessment questionnaire and their answers where 11 expert musicians were asked to rate musical pieces recorded based on:</p> <ol> <li> <p>Objective valence & arousal they felt the piece had;</p> </li> <li> <p>Felt valence & arousal during playing.</p> </li> </ol> </li> </ol> <p> </p> <p>For a more detailed explanation of the dataset, its recording procedure, and its contents, see </p> <p>L. Turchet, B. O'Sullivan, R. Ortner & C. Gugher (2024). Emotion Recognition of Playing Musicians from EEG, ECG, and Acoustic Signals. IEEE Transactions on Human-Machine Systems.</p> <p><strong> </strong></p> <h2>File Listing</h2> <p>The following files are available (each explained in more detail below):</p> <div> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Format</p> </td> <td> <p>Contents</p> </td> </tr> <tr> <td> <p>EEG_ECG_data_for_each_musician</p> </td> <td> <p>mat</p> </td> <td> <p>This folder contains the raw EEG, ECG, & GSR data for all 11 musicians for each piece they played as well as a resting state recording, which was recorded while a neutral audio stimulus was played.</p> </td> </tr> <tr> <td> <p>audio_data_for_each_musician</p> </td> <td> <p>wav, JSON, csv</p> </td> <td> <p>This folder contains 3 subfolders: <br>1) wav_audio_files_original: This is the raw audio data recorded for each musician and includes all 56 pieces included in the paper reported above, please see below regarding rejected trials.</p> <p>2) wav_audio_files (original split_into_3_parts): Here, the 56 pieces are appropriately split into 3 separate parts.<br><br>3) analysis_audio_files: This folder contains the acoustic features extracted, 1714 acoustic features were extracted from each split trial. Each result is stored in .JSON, however, the collated results can be seen in all_results.csv.</p> </td> </tr> <tr> <td> <p>self_report_questionnaire</p> </td> <td> <p>pdf, xls</p> </td> <td> <p>Two files exist in this folder:<br>1. The self-reported questionnaire given to the musicians of the questionnaire during the experiment.<br>2. </p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p> <h2>File Details</h2> <p><strong> </strong></p> <h3>EEG_ECG_data_for_each_musician</h3> <p>These are the original raw data recordings. EEG data were recorded using a g.GAMMAcap2 by g.tec Medical Engineering, a 64-channel cap with g.SCARABEO active electrodes, with two g.GAMMAsys reference active ear clip electrodes. Two g.GAMMAbox electrode connector boxes were used to connect the active electrodes to two g.USBamp biosignal amplifiers with a sampling frequency of 256 Hz.<br><br>The following 31 EEG channels were used: Fp1, Fp2, AFz, AF3, AF4, AF7, AF8, Fz, F3, F4, F7, F8, Cz, C3, C4, CP3, CP4, CP5, CP6, P1, P2, P3, P4, P5, P6, P7, P8, PO7, PO8, O1, and O2. AFz was used as a ground electrode and Cz was used as a re-reference electrode. The right-side ear clip electrode was used as a reference electrode. </p> <p><br>ECG data were recorded using a single g.GAMMAclip active electrode clip connected directly to the g.GAMMAbox, sharing the same ground electrode with the EEG cap and placed on position V4 of the subjects.<br><br>GSR data was recorded using the g.GSRsensor² box which contains two small dry electrodes placed underneath the participant’s toes (due to the amount of hand movement required for playing an instrument). The g.GSRsensor² was connected directly to a g.GAMMAbox, using jumper cables connected to different ports in the g.USBAMPs to share the same reference and ground electrodes as the EEG cap. </p> <p><strong> </strong></p> <p>The locations of the channels and their corresponding number in the raw data is as follows:</p> <div> <table> <tbody> <tr> <td> <p>Channel Number</p> </td> <td> <p>Channel Name</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>Time series</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>AF3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>AF4</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>AF7</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>AF8</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>CP3</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>CP4</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>CP5</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>CP6</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>P1</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>P2</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>P5</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>P6</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>P7</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>P8</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>O1</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>O2</p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>Cz</p> </td> </tr> <tr> <td> <p>19</p> </td> <td> <p>Fp1</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Fp2</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>F3</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>F4</p> </td> </tr> <tr> <td> <p>23</p> </td> <td> <p>F7</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>F8</p> </td> </tr> <tr> <td> <p>25</p> </td> <td> <p>C3</p> </td> </tr> <tr> <td> <p>26</p> </td> <td> <p>C4</p> </td> </tr> <tr> <td> <p>27</p> </td> <td> <p>P3</p> </td> </tr> <tr> <td> <p>28</p> </td> <td> <p>P4</p> </td> </tr> <tr> <td> <p>29</p> </td> <td> <p>PO7</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>PO8</p> </td> </tr> <tr> <td> <p>31</p> </td> <td> <p>AFz</p> </td> </tr> <tr> <td> <p>32</p> </td> <td> <p>GSR</p> </td> </tr> <tr> <td> <p>33</p> </td> <td> <p>ECG</p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p> <h3>audio_data_for_each_musician/wav_audio_files_original</h3> <p>This folder contains all of the raw audio files recorded during the experiment. All pieces were recorded using the software Audacity and exported as WAV files encoded with a bit depth of 32-bits and a sampling rate of 44.1 kHz. 56 of the included trials are present in this folder.</p> <p><strong> </strong></p> <h3>audio_data_for_each_musician/wav_audio_files (original split_into_3_parts)</h3> <p>This folder contains the above mentioned 56 raw audio pieces separated into 3 appropriately sized recordings.</p> <p><strong> </strong></p> <p>audio_data_for_each_musician/analysis_audio_files<br><br>This folder contains the 1714 acoustic features from each of the 3 separated trials from 56 accepted pieces, these are denoted by MS_ followed by the order in which the musicians were recorded and the order in which the trails were split, the individual features are in JSON format. Within this folder, we have collated all results in a csv file (all_results.csv) where the columns show the intended emotion, trial name and number, followed by the names of the acoustic features. </p> <p><strong> </strong></p> <p>self_report_questionnaire</p> <p>This pdf is the questionnaire which each musician was given following each trial relating to the emotions communicated and felt during each trial. Most* questions in the questionnaire were multiple-choice and speak pretty much for themselves. The answers for which were collated and are described below.</p> <p><strong> </strong></p> <p>self_report_questionnaire_anwsers</p> <p>This .xls file contains the results of all the musicians self-reported ratings from the above described questionnaire. </p> <div> <table> <tbody> <tr> <td> <p>Column name</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Subject</p> </td> <td> <p>The subject code of the musician, denoted by MS_</p> </td> </tr> <tr> <td> <p>recording_filename</p> </td> <td> <p>The name of the trial denoted by MS_01_ followed by the trial number.</p> </td> </tr> <tr> <td> <p>intended_emotion</p> </td> <td> <p>The intended emotion the musician was instructed to communicate. The emotions are as follows:</p> <ol> <li> <p>Angry</p> </li> <li> <p>Sad</p> </li> <li> <p>Relaxed</p> </li> <li> <p>Happy</p> </li> </ol> <p>They are intended to be reported by using the valence-arousal space.</p> </td> </tr> <tr> <td> <p>valence_communicated</p> </td> <td> <p>The valence rating (integer between 1 and 5), that participants were asked to objectively rate how they thought the music played would be perceived. </p> </td> </tr> <tr> <td> <p>arousal_communicated</p> </td> <td> <p>As above, but relating to arousal rather than valence.</p> </td> </tr> <tr> <td> <p>valence_felt</p> </td> <td> <p>The valence rating (integer between 1 and 5), that participants were asked to rate how they felt while playing the piece.</p> </td> </tr> </tbody> </table> </div> <p> </p> <div> <table> <tbody> <tr> <td> <p>arousal_felt</p> </td> <td> <p>As above, but relating to arousal rather than valence.</p> </td> </tr> <tr> <td> <p>instrument_played</p> </td> <td> <p>The instrument played for each trial.</p> </td> </tr> </tbody> </table> </div> <p><strong><br><br></strong></p>
Multimodal Epigenetic Sequencing Analysis (MESA) of Cell-free DNA for Non-invasive Colorectal Cancer Detection
<p>Processed data (feature-by-sample matrices) of non-disruptive bisulfite-free methylation sequencing for cfDNA samples from 4 clinical cohorts (Cohort 1, Cohort 2, Cohort 3, and cfTAPS dataset). Codes used to repeat the results in our paper can be found https://rpubs.com/LiYumei/926228 and https://github.com/ChaorongC/MESA. </p>
Data from: Brittle sedimentary strata focus a multimodal depth distribution of seismicity during hydraulic fracturing in the Sichuan basin, southwest China
<p>The number of background earthquakes (<em>M<sub>L</sub></em> ≥ 0) in the southern Sichuan basin, southwest China, has increased thirtyfold as a result of hydraulic fracturing. Background events are originally deep (4-6 <em>km</em>) within the sedimentary section but build into a multimodal distribution both at depth and in the shallow stimulated reservoir (2-4 <em>km</em>) - representing a counterpoint to the usual triggering of seismicity on deep sub-reservoir basement faults. Surprisingly, the largest events (<em>M<sub>L</sub></em> ≥ 3) evolve in the deep sedimentary strata (4-6 <em>km</em>) that are hydraulically isolated from the injection zone (2-4 <em>km</em>) by low permeability layers. We evaluate the friction-stability rheology of the strata within the full stratigraphic section to define the feasibility of nucleation within these shallow and deep strata. These show velocity-neutral to velocity-weakening behavior in the shallow reservoir transitioning to more strongly velocity-weakening with increase in both depth and temperature. Poroelastic stress calculations confirms that stress transfer, rather than transmitted fluid pressures, are capable of directly reactivating critically-stressed faults at depth, with fluid pressures the triggering source within the shallow reservoir.</p>
Multimode ion-photon entanglement over 101 kilometers
<p>Data and analysis code for the paper entitled "Multimode ion-photon entanglement over 101 kilometers" which is to be published in PRX QUantum journal. </p> <p>Description of the content is provided in the README.txt. Analysis of the data was executed in matlab 2023b.<br><br>Abstract of the paper that results from this dataset:<br>"A three-qubit quantum network node based on trapped atomic ions is presented. The ability to<br>establish entanglement between each individual qubit in the node and a separate photon that has<br>travelled over a 101 km-long optical fiber is demonstrated. By sending those photons through the<br>fiber in close succession, a remote entanglement rate is achieved that is greater than when using<br>only a single qubit in the node. Once extended to more qubits, this multimode approach can be a<br>useful technique to boost entanglement distribution rates in future long-distance quantum networks<br>of light and matter."</p>
PsySuite: Performing multimodal psychophysical testing within the Android environment
<p>Data used for either the hardware or the behavioral validation of the Android APP <em>PsySuite</em>.</p>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
Multimodal mismatch responses in mouse auditory cortex
<div>All raw data and Matlab code necessary to produce the figures of https://elifesciences.org/reviewed-preprints/95398</div> <div> </div> <div> </div>
Transformer-based graphical neural network with expert experience multimodal learning (TGEML) framework: a nanocomposite performance predictor
<p>TGEML is a novel multimodal nanocomposite processing framework consists of a polymer multimodal featurizer called TGEML-polymer and a nanoparticle expert experience featurizer called TGEML-nano.</p>
Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry
<p>This repository contains the data associated with: "Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry" (see here: <a href="https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset/">https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset/</a>)</p>
PCLMM: A multimodal dataset for Patronizing and Condescending Language detection
<p>The PCLMM dataset is sourced from Bilibili, the largest online community for young people in China. Our work aims to uncover microaggressions targeted at vulnerable groups, including discriminatory and patronizing language expressions, as well as facial expression features. PCLMM contains six main vulnerable communities :</p> <ol> <li>Disabled individuals</li> <li>Women</li> <li>The elderly</li> <li>Children</li> <li>Single-parent families</li> <li>Low-income groups</li> </ol> <p>The PCLMM dataset contains 715 videos, totaling 21 hours of content, with an average video length of 1.80 mins and a frame rate of 30 FPS, comprising 2.3M frames. Approximately 27.4% of the videos were labeled as patronizing (label-1), aligning with the distribution of PCL data on internet platforms.</p> <p>If you would like to learn more about our PCL field (a branch of toxic speech detection) and PCLMM dataset, please refer to this paper, accepted by ICASSP 2025: </p> <p><a href="https://ieeexplore.ieee.org/abstract/document/10890580">https://ieeexplore.ieee.org/abstract/document/10890580</a></p> <p>The code implementation of the paper can be found in our repository: <a href="https://github.com/dut-laowang/PCLMM" target="_new" rel="noopener">https://github.com/dut-laowang/PCLMM</a></p> <p><strong>Updation</strong></p> <p><em><strong>February 9, 2025</strong></em> – The Annotation_Subset.csv containing the original video links has been updated.</p>
Function of a multimodal signal: a multiple hypothesis test using a robot frog
<p>1. Multimodal communication may evolve because different signals may convey information about the signaller (content-based selection), increase efficacy of signal processing or transmission through the environment (efficacy-based selection), or modify the production of a signal or the receiver's response to it (inter-signal interaction selection).</p> <p>2. To understand the function of a multimodal signal (aggressive calls + toe flags) emitted by males of the frog Crossodactylus schmidti during territorial contests, we tested two hypotheses related to content-based selection (quality and redundant signal), one related to efficacy-based selection (efficacy backup), and one related to inter-signal interaction selection (context). For each hypothesis we derived unique predictions based on the biology of the study species.</p> <p>3. In a natural setting, we exposed resident males to a robot frog simulating aggressive calls (acoustic stimulus) and toe flags (visual stimulus), combined and in isolation, and measured quality-related traits from males and local levels of background noise and light intensity.</p> <p>4. Our results provide support to the context hypothesis, as toe flags (the context signal) are insufficient to elicit a receiver's response on their own. However, when toe flags are emitted together with aggressive calls, they evoke in the receiver qualitatively and quantitatively different responses from that evoked by aggressive calls alone. In contrast, we found no evidence that toe flags and aggressive calls provide complementary or redundant information about male quality, which are key predictions of the quality and redundant signal hypotheses, respectively. Finally, the multimodal signal did not increase the receiver's response across natural gradients of light and background noise, a key prediction of the efficacy backup hypothesis.</p> <p>5. Toe flags accompanying aggressive calls seem to provide contextual information that modify the receiver's response in territorial contests. We suggest this contextual information is increased motivation to escalate the contest, and discuss the benefits to the signallers and receivers of adding a contextual signal to the aggressive display. Examples of context-dependent multimodal signals are rare in the literature, probably because most studies focus on single hypotheses assuming content- or efficacy-based selection. Our study highlights the importance of considering multiple selective pressures when testing multimodal signal function. </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.