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1,389 results for “multimodality”
DATASET FOR: A multimodal spectroscopic approach combining mid-infrared and near-infrared for discriminating Gram-positive and Gram-negative bacteria
<h4>Description:</h4> <p>This dataset comprises a comprehensive set of files designed for the analysis and 2D correlation of spectral data, specifically focusing on ATR and NIR spectra. It includes MATLAB scripts and supporting functions necessary to replicate the analysis, as well as the raw datasets used in the study. Below is a detailed description of the included files:</p> <ol> <li> <p><strong>Data Analysis</strong>:</p> <ul> <li><strong>File Name</strong>: <code>Data_Analysis.mlx</code></li> <li><strong>Description</strong>: This MATLAB Live Script file contains the main script used for the classification analysis of the spectral data. It includes steps for preprocessing, analysis, and visualization of the ATR and NIR spectra.</li> </ul> </li> <li> <p><strong>2D Correlation Data Analysis</strong>:</p> <ul> <li><strong>File Name</strong>: <code>Data_Analysis_2Dcorr.mlx</code></li> <li><strong>Description</strong>: This MATLAB Live Script file is similar to the primary analysis script but is specifically tailored for performing 2D correlation analysis on the spectral data. It includes detailed steps and code for executing the 2D correlation.</li> </ul> </li> <li> <p><strong>Functions</strong>:</p> <ul> <li><strong>Folder Name</strong>: <code>Functions</code></li> <li><strong>Description</strong>: This folder contains all the necessary MATLAB function files required to replicate the analyses presented in the scripts. These functions handle various preprocessing steps, calculations, and visualizations.</li> </ul> </li> <li> <p><strong>Datasets</strong>:</p> <ul> <li><strong>File Names</strong>: <code>ATR_dataset.xlsx</code>, <code>NIR_dataset.xlsx</code>, <code>Reference_data.csv</code></li> <li><strong>Description</strong>: These Excel files contain the raw spectral data for ATR and NIR analyses, as well as reference datasets. Each file includes multiple sheets with detailed measurements and metadata.</li> </ul> </li> </ol> <h4>Usage Notes:</h4> <ul> <li><strong>Software Requirements</strong>: <ul> <li>MATLAB is required to run the .mlx files and utilize the functions.</li> <li><strong>PLS_Toolbox</strong>: Necessary for certain preprocessing and analysis steps.</li> <li><strong>MIDAS 2010</strong>: Available at <a href="https://www.mathworks.com/matlabcentral/fileexchange/32384-midas-2010" target="_new" rel="noreferrer">MIDAS 2010</a>, required for the 2D correlation analysis.</li> </ul> </li> <li><strong>Replication</strong>: Users can replicate the analyses by running the <code>Data_Analysis.mlx</code> and <code>Data_Analysis_2Dcorr.mlx</code> scripts in MATLAB, ensuring that the <code>Functions</code> folder is in the MATLAB path.</li> <li><strong>Data Handling</strong>: The datasets are provided in .xlsx format, which can be easily imported into MATLAB or other data analysis software.</li> </ul>
A multimodal data-set of a unidirectional glass fibre reinforced polymer composite
<p>Please cite the following article when using the data-sets hereby shared:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. A multimodal data-set of a unidirectional glass fibre reinforced polymer composite. <em>Data in brief</em>, <em>18</em>, pp.1388-1393.</p> <p>These data-sets were used for validating the use of X-ray tomography and our dictionary-based probabilistic method for detection of individual fibres, for more information see the following article:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. Statistical validation of individual fibre segmentation from tomograms and microscopy. <em>Composites Science and Technology</em>, <em>160</em>, pp.208-215.</p>
Mid-crustal low-velocity zones beneath Southeastern Coastal China revealed by multimodal ambient noise tomography: insights into Mesozoic Magmatic Activities
<p><span>It contains Rayleigh dispersion data </span><span>and</span><span> a three</span><span>-</span><span>dimensional crustal shear</span><span>-</span><span>wave velocity model of the Southeastern Coastal China </span><span>(</span><span>SCC</span><span>)</span><span>.</span></p>
Multimodal Sensor Dataset from Wood/Forest Environment - Adões
<p>This forestry robotic dataset consists of a <strong>forest scenario</strong> with a relatively restricted and blocked trajectory, <a href="https://www.google.pt/maps/place/40%C2%B017'04.0%22N+8%C2%B027'25.7%22W/@40.2845835,-8.4577709,166m/data=!3m1!1e3!4m4!3m3!8m2!3d40.2844305!4d-8.4571394?hl=pt-PT&entry=ttu&g_ep=EgoyMDI0MDkwOC4wIKXMDSoASAFQAw%3D%3D"> Adões, Coimbra, Portugal</a> (40º28'44.305''N; -8º45'71.394''W).<br>The dataset contains rosbags information with different rostopics of various sensors, with a total distance covered of approximately 620 meters.<br>The dataset was broken into rosbags with a maximum storage of 10GB each.</p> <p>The recorded topics were as follows:</p> <ul> <li>Velarray M1600 LiDAR point clouds data (sensor_msgs/PointCloud2) @20Hz;</li> <li>Realsense Intel D435i compressed image data, compressed depth info, alongside camera info (sensor_msgs/CompressedImage + sensor_msgs/CompressedImage + sensor_msgs/CameraInfo) @30Hz;</li> <li>Mynt Eye S1030 left camera data alongside camera info (sensor_msgs/Image + sensor_msgs/CameraInfo) @20Hz;</li> <li>Mynt Eye S1030 right camera data alongside camera info (sensor_msgs/Image + sensor_msgs/CameraInfo) @20Hz;</li> <li>IMU sensor data, alongside magnetometer data (sensor_msgs/Imu + sensor_msgs/MagneticField) @100Hz;</li> <li>RTK GNSS information on the trajectory taken (sensor_msgs/NavSatFix) @1Hz.</li> </ul>
Multimodal Sensor Dataset from Wood/Forest Environment - iParque
<p>This forestry robotic dataset comprises a <strong>woodland scenario</strong> with a relatively open sky view, <a href="https://www.google.pt/maps/place/40%C2%B010'35.3%22N+8%C2%B028'07.2%22W/@40.1765628,-8.468883,70m/data=!3m1!1e3!4m4!3m3!8m2!3d40.1764826!4d-8.4686631?hl=pt-PT&entry=ttu&g_ep=EgoyMDI0MDkwOC4wIKXMDSoASAFQAw%3D%3D">iParque Park, Coimbra, Portugal</a> ( 40º17'64.826''N; -8º46'86.631''W).<br>The dataset contains information on rosbags with different rostopics of various sensors, with a total distance covered of approximately 950 meters.<br>The dataset was broken into rosbags with a maximum storage of 10GB each.</p> <p>The recorded topics were as follows:</p> <ul> <li>Velarray M1600 LiDAR point clouds data (sensor_msgs/PointCloud2) @20Hz;</li> <li>Realsense Intel D435i compressed image data, compressed depth info, alongside camera info (sensor_msgs/CompressedImage + sensor_msgs/CompressedImage + sensor_msgs/CameraInfo) @30Hz;</li> <li>Mynt Eye S1030 left camera data alongside camera info (sensor_msgs/Image + sensor_msgs/CameraInfo) @20Hz;</li> <li>Mynt Eye S1030 right camera data alongside camera info (sensor_msgs/Image + sensor_msgs/CameraInfo) @20Hz;</li> <li>IMU sensor data, alongside magnetometer data (sensor_msgs/Imu + sensor_msgs/MagneticField) @100Hz;</li> <li>RTK GNSS information on the trajectory taken (sensor_msgs/NavSatFix) @1Hz.</li> </ul>
Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base
<p>Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base</p>
MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks
<p>This dataset was used in the experimental evaluation of the MUSE route planning algorithm. It encompasses the Ile-de-France multimodal network. The dataset contains:</p> <p>1. The raw osm file of the Ile-de-France region from OpenStreetMap.</p> <p>2. The raw GTFS data for the public transit network.</p> <p>3. The multimodal graph based on 5 partitions: 100, 200, 300, 400, and 500 cells.</p> <p>4. The Nondeterministic Finite Automata (NFA) used during the preprocessing and query stages of MUSE.</p> <p>5. The graph overlay evaluated during the preprocessing stage of MUSE.</p> <p>To test MUSE and review the details of this dataset, please visit https://github.com/aminefalek/muse</p>
Pied tamarins go multimodal in response to anthropogenic noise
<p><span>Sounds produced by human activities are often loud and may mask acoustic signals used by other species for communication. To circumvent this, animals may use various strategies, including shifting modality completely or complementing acoustic information by also using another modality of communication. Here we tested the overlooked multimodal shift hypothesis using pied tamarins (<i>Saguinus</i> <i>bicolor</i>) as models. We predicted that in noisier areas the species would exhibit more scent marking behaviour (i.e., olfactory communication), while reducing the emission of long calls (i.e., acoustic communication). We collected information on vocal and scent marking behaviour in nine groups of wild pied tamarins in Manaus, Brazil. We found that scent marking occurrence increased with noise amplitude, though the number of long calls did not change. Thus, our results do not suggest a shift between channels but complementation of information, where scent marking may counteract against the compromising of the acoustic channel by the urban environment. This is an interesting result from a conservation perspective and supports ongoing efforts to create ecological corridors in Manaus, Brazil; once habitat connection is established, pied tamarins may be capable of coping with city noise to communicate with conspecifics, a key tenet of species survival.</span></p>
Datasets for Evaluation of Multimodal Image Registration
<p><strong>Description</strong></p> <ul> <li><strong>Aerial data</strong></li> <li>The Aerial dataset is divided into 3 sub-groups by IDs: {7, 9, 20, 3, 15, 18}, {10, 1, 13, 4, 11, 6, 16}, {14, 8, 17, 5, 19, 12, 2}. Since the images vary in size, each image is subdivided into the maximal number of equal-sized non-overlapping regions such that each region can contain exactly one 300x300 px image patch. Then one 300x300 px image patch is extracted from the centre of each region. The particular 3-folded grouping followed by splitting leads to that each evaluation fold contains 72 test samples. <ul> <li> <p>Modality A: Near-Infrared (NIR)</p> </li> <li> <p>Modality B: three colour channels (in B-G-R order)</p> </li> </ul> </li> <li><strong>Cytological data</strong></li> <li>The Cytological data contains images from 3 different cell lines; all images from one cell line is treated as one fold in 3-folded cross-validation. Each image in the dataset is subdivided from 600x600 px into 2x2 patches of size 300x300 px, so that there are 420 test samples in each evaluation fold. <ul> <li> <p>Modality A: Fluorescence Images</p> </li> <li> <p>Modality B: Quantitative Phase Images (QPI)</p> </li> </ul> </li> <li><strong>Histological dataset</strong></li> <li>For the Histological data, to avoid too easy registration relying on the circular border of the TMA cores, the evaluation images are created by cutting 834x834 px patches from the centres of the original 134 TMA image pairs. <ul> <li> <p>Modality A: Second Harmonic Generation (SHG)</p> </li> <li> <p>Modality B: Bright-Field (BF)</p> </li> </ul> </li> </ul> <p>The evaluation set created from the above three publicly available 2D datasets consists of images undergone 4 levels of (rigid) transformations of increasing size of displacement. The level of transformations is determined by the size of the rotation angle θ and the displacement tx & ty, detailed in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets">this table</a>. Each image sample is transformed exactly once at each transformation level so that all levels have the same number of samples.</p> <ul> <li><strong>Radiological data</strong></li> <li>The Radiological dataset is divided into 3 sub-groups by patient IDs: {109, 106, 003, 006}, {108, 105, 007, 001}, {107, 102, 005, 009}. Since the Radiological dataset is non-isotropic (and also of varying resolution), it is resampled using B-spline interpolation to 1 mm<sup>3</sup> cubic voxels, taking explicit care to not resample twice; displaced volumes are transformed and resampled in one step. <ul> <li> <p>Modality A: T1-weighted MRI</p> </li> <li> <p>Modality B: T2-weighted MRI</p> </li> </ul> </li> </ul> <p>(Run <a href="https://github.com/MIDA-group/MultiRegEval/blob/master/utils/make_rire_patches.py"><code>make_rire_patches.py</code></a> to generate the sub-volumes.)</p> <p>Reference sub-volumes of size 210x210x70 voxels are cropped directly from centres of the (non-displaced) resampled volumes. Similarly as for the aforementioned 2D datasets, random (uniformly-distributed) transformations are composed of rotations θx, θy ∈ [-4, 4] degrees around the x- and y-axes, rotation θz ∈ [-20, 20] degrees around the z-axis, translations tx, ty ∈ [-19.6, 19.6] voxels in x and y directions and translation tz ∈ [-6.5, 6.5] voxels in z direction. 40 rigid transformations of increasing sizes of displacement are applied to each volume. Transformed sub-volumes, of size 210x210x70 voxels, are cropped from centres of the transformed and resampled volumes.</p> <p> </p> <p>In total, it contains 864 image pairs created from the aerial dataset, 5040 image pairs created from the cytological dataset, 536 image pairs created from the histological dataset, and metadata with scripts to create the 480 volume pairs from the radiological dataset. Each image pair consists of a reference patch <span class="math-tex">\(I^{\text{Ref}}\)</span> and its corresponding initial transformed patch <span class="math-tex">\(I^{\text{Init}}\)</span> in both modalities, along with the ground-truth transformation parameters to recover it.</p> <p>Scripts to calculate the registration performance and to plot the overall results can be found in <a href="https://github.com/MIDA-group/MultiRegEval">https://github.com/MIDA-group/MultiRegEval</a>, and instructions to generate more evaluation data with different settings can be found in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data">https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>In the <code>*.zip</code> files, each row in <code>{Zurich,Balvan}_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> or <code>Eliceiri_patches/patch_tlevel[1-4]/info_test.csv</code> provides the information of an image pair as follow:</p> <ul> <li> <p>Filename: identifier(ID) of the image pair</p> </li> <li> <p>X1_Ref: x-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y1_Ref: y-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X2_Ref: x-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y2_Ref: y-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X3_Ref: x-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y3_Ref: y-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X4_Ref: x-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y4_Ref: y-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X1_Trans: x-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y1_Trans: y-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X2_Trans: x-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y2_Trans: y-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X3_Trans: x-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y3_Trans: y-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X4_Trans: x-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y4_Trans: y-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Displacement: mean Euclidean distance between reference corner points and transformed corner points</p> </li> <li> <p>RelativeDisplacement: the ratio of displacement to the width/height of image patch</p> </li> <li> <p>Tx: randomly generated translation in the x-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>Ty: randomly generated translation in the y-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleDegree: randomly generated rotation in degrees to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleRad: randomly generated rotation in radian to synthesise the transformed patch I<sub>Init</sub></p> </li> </ul> <p>In addition, each row in <code>RIRE_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> has following columns:</p> <ul> <li>Z1_Ref: z-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></li> <li>Z2_Ref: z-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></li> <li>Z3_Ref: z-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></li> <li>Z4_Ref: z-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></li> <li>Z1_Trans: z-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></li> <li>Z2_Trans: z-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></li> <li>Z3_Trans: z-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></li> <li>Z4_Trans: z-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></li> <li>(...and similarly, coordinates of the 5th-8th corners)</li> <li>Tz: randomly generated translation in z-direction to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeX: randomly generated rotation around X-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadX: randomly generated rotation around X-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeY: randomly generated rotation around Y-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadY: randomly generated rotation around Y-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeZ: randomly generated rotation around Z-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadZ: randomly generated rotation around Z-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> </ul> <p> </p> <p><strong>Naming convention</strong></p> <ul> <li><strong>Aerial Data</strong> <ul> <li> <pre> zh{ID}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>zh5_03_02_R.png</code> indicates the <em>Reference </em>patch of the <em>3rd row</em> and <em>2nd column</em> cut from the image with ID <code>zh5</code>.</li> </ul> </li> <li><strong>Cytological data</strong> <ul> <li> <pre> {{cellline}_{treatment}_{fieldofview}_{iFrame}}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>PNT1A_do_1_f15_02_01_T.png</code> indicates the <em>Transformed </em>patch of the <em>2nd row</em> and <em>1st column</em> cut from the image with ID <code>PNT1A_do_1_f15</code>.</li> </ul> </li> <li><strong>Histological data</strong> <ul> <li> <pre> {ID}_{ReferenceOrTransformed}.tif</pre> </li> <li>Example: <code>1B_A4_T.tif</code> indicates the <em>Transformed </em>patch cut from the image with ID <code>1B_A4</code>.</li> </ul> </li> </ul> <ul> <li><strong>Radiological Data</strong> <ul> <li> <pre> patient_{ID}_{iTransform}_T.mhd</pre> </li> <li> <pre> patient_{ID}_R.mhd </pre> </li> <li>Example: <code>patient_003_8_T.mhd</code> indicates the sub-volume <em>Transformed </em>with the <em>8th random transformation</em> cut from the volume with patient ID <code>003</code>; <code>patient_003_R.mhd</code> indicates the <em>Reference </em>sub-volume the volume with patient ID <code>003</code>.</li> </ul> </li> </ul> <p> </p> <p>This dataset was originally produced by the authors of <em><a href="https://arxiv.org/abs/2103.16262">Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study</a></em>.</p>
Multimodal faces dataset for PracticalMEEG handson tutorials
<p>The dataset analyzed in PracticalMEEG tutorials is part of a dataset recorded by Rik Hanson and colleagues. During the hands-on sessions we will mainly work with the MEG data of a single representative subject, for the group statistics we will work with source-level processed data from all subjects.</p> <p>The “mmfaces” dataset contains EEG, MEG, functional MRI and structural MRI data from research participants that were recorded in multiple runs of a simple task performed on a large number of Famous, Unfamiliar and Scrambled faces. It is described in more detail the data descriptor publication <a href="https://www.nature.com/articles/sdata20151">doi:10.1038/sdata.2015.1</a> and analyzed in detail in <a href="http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00076/abstract">doi:10.3389/fnhum.2011.00076</a>.</p> <p>The original multimodal dataset included simultaneous MEG/EEG recordings on 19 healthy subjects. In the original study, three subjects (sub001, sub005, sub016) were excluded from further analysis.</p> <p>The dataset used to be available from the MRC-CBU FTP server, but is nowadays maintained on <a href="https://openneuro.org/datasets/ds000117">OpenNeuro</a>.</p> <p>Stimulation details</p> <ul> <li>The start of a trial was indicated with a fixation cross of random duration between 400 to 600 ms</li> <li>The face stimuli was superimposed on the fixation cross for a random duration of 800 to 1,000 ms</li> <li>Inter-stimulus interval of 1,700 ms comprised a central white circle</li> <li>Two types of stimulation patterns: <ul> <li>Immediate: The image was presented consecutively</li> <li>Long: The two images were presented with 5-15 intervening stimuli</li> </ul> </li> <li>For the purposes of our analysis, we treat these two stimulation patterns of stimuli together</li> <li>To maintain attention, subjects were asked to judge the symmetry of the image and respond with a keypress</li> </ul> <p>MEG/EEG acquisition details</p> <p>The MEG data consist of 102 magnetometers and 204 planar gradiometers from a Neuromag/Elekta/Megin VectorView system. The same system was used to simultaneously record EEG data from 70 electrodes (using a nose reference), which are stored in the same “FIF” format file. The above FTP site includes a raw .fif file for each run/subject, but also a second .fif file in which the MEG data have been “cleaned” using Signal-Space Separation as implemented in MaxFilter 2.1.</p> <p>A Polhemus was used to digitize three fiducial points and a large number of other points across the scalp, which can be used to co-register the M/EEG data with the structural MRI image. Six runs of approximately 10 minutes each were acquired for each subject, while they judged the left-right symmetry of each stimulus (face or scrambled), leading to nearly 300 trials in total for each of the 3 conditions.</p> <ul> <li>Sampling frequency: 1100 Hz</li> <li>Stimulation triggers: The trigger channel is STI101 with the following event codes:</li> <li>Famous faces: 5 (first), 6 (immediate), and 7 (long)</li> <li>Unfamiliar faces: 13 (first), 14 (immediate), and 15 (long)</li> <li>Scrambled faces: 17 (first), 18 (immediate), and 19 (long)</li> <li>Sensors <ul> <li>102 magnetometers</li> <li>204 planar gradiometers</li> <li>70 electrodes recorded with a nose reference (Easycap conforming to extended 10-20% system)</li> <li>Two sets of bipolar electrodes were used to measure vertical (left eye; EEG062) and horizontal Electro-oculograms (EEG061). Another set was used to measure ECG (EEG063)</li> </ul> </li> <li>A fixed 34 ms delay exists between the appearance of a trigger in the trigger channel STI101 and the appearance of the stimulus on the screen</li> </ul> <p>MRI acquisition details</p> <p>The MRI data were acquired on a 3T Siemens TIM Trio, and include a 1x1x1mm T1-weighted structural MRI (sMRI) as well as a large number of 3x3x4mm T2*-weighted functional MRI (fMRI) EPI volumes acquired during 9 runs of the same task (performed by same subjects with different set of stimuli on a separate visit). (The FTP site also contains DTI and ME-FLASH MRI images from the same subject, which could be used for improved head modeling for example, but these are not used here.) For full description of the data and paradigm, see README.txt on the FTP site or <a href="http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00076/abstract">Wakeman & Henson</a>.</p>
Data from: Functionally redundant multimodal predator cues elicit changes in prey foraging behavior
<p><span>Many prey species can assess the risk of predation from information acquired through different sensory systems. For many animals, this information is detected with sensory organs specialized for visual (sight) or chemical (smell or taste) stimuli. It is unclear, however, whether information acquired through multiple sensory systems is functionally redundant or interchangeable, especially if the message is the same. Here we assess prey response to unimodal visual and chemical cues as well as multimodal (visual + chemical) cues. We specifically test if a foraging individual shows a stronger behavioral response to risk when they can perceive that risk through multimodal versus unimodal cues. To do this, we measured the functional response (prey abundance-foraging rate relationship) of Tibellus oblongus spiders foraging on midges while exposing them to visual stimuli, chemical stimuli, or a combination of both visual and chemical stimuli from potential predators. We then determined if the spider's functional response for the multimodal treatment differed more strongly from a control treatment than from either unimodal treatment. We found that under any simulated predation risk (multimodal and both unimodal), T. oblongus spiders showed longer handling times than in control groups without risk. </span><span>However, we saw no elevated anti-predator response in the multimodal treatment, suggesting that information from visual and chemical modalities is interchangeable and sufficient to indicate reliably predation risk. </span></p>
Foraging preferences and interspecific competition generate multimodal complexity-stability relationships in an adaptive food-web framework
<p><span>Ecological theory predicts that complex ecological networks are unstable and are unlikely to persist, despite many empirical studies of such complexity in nature. To resolve real complexity-stability relationships, coupling population dynamics and trait dynamics is considered to be an important way to understand the long-term stability of ecological community assemblages. However, modelling eco-evolutionary dynamics in ecologically realistic networks is still a challenge. Here, we establish an adaptive food web model to evaluate the complexity-stability debate in a mutualist-exploiters-specialist forager-generalist forager system. Our theoretical model predicts that the connectance-stability relationship may show positive monotonic (/), negative monotonic (\), peaked (∩) and double-peaked (oscillatory) patterns. Moreover, the double-peaked pattern is only obtained when both the adaptation intensity and interspecific competition are high, which may explain no complexity-stability relationships revealed in empirical data. Finally, we deduce that foraging adaptation alters positive and/or negative feedback loops to affect the stability of real food webs.</span></p>
PiH Exception strategies Multimodal Database
<p>The database contains data obtained from executions of a peg-in-hole task (using the Cranfiled benchmark). We used a Franka Emika Research 3 robot, an ATI Nano25 force/torque sensor, and an Intel Realsense D435 RGB-D camera.</p> <p>The database is structured by its offset from the in-hole position. The folders are organized according to the following structure:</p> <p>In the first folder level, the positional offset is specified. The orientational (rpy) offset is defined in the second folder level. At the third level, we have two folders:<br> - one for the the trajectory, that led to the occurrence of the error, and<br> - one for the human-demonstrated trajectories, that show possible correction strategies.</p> <p>Each entry is formatted as MATLAB data file (`.mat`) with a states structure, that contains:<br> - reference and measured target pose in Cartesian space (positions and quaternions; sampled at 100Hz),<br> - raw force-torque sensor data and force-torque data measured in the robot-wrist (sampled at 100Hz),<br> - RGB image sequence (sampled at 10Hz),<br> - point-cloud sequence (sampled at 10Hz).</p> <p><br> We use the following labeling for file names.</p> <p>The first part denotes positional offset:<br> - x10 ... +10mm in $x$-direction from target position<br> - xm10 ... -10mm in $x$-direction from target position<br> - y10 ... +10mm in $y$-direction from target position<br> - ym10 ... -10mm in $y$-direction from target position</p> <p>The second part denotes orientation offset:<br> - x10 ... +10 degrees around $x$-axis<br> - y10 ... +10 degrees around $y$-axis<br> - z10 ... +10 degrees around $z$-axis</p> <p>The ending `.cor.N` stands for the $N$-th correction trajectories, and `.error` for the trajectory where an error occurred.</p> <p>Example: The file `x0y10_x0y0z0.cor.1.mat` contains data from a correction trajectory where at the onset of the error, the peg is displaced from the target pose for 0 mm in $x$-direction, 10 mm in $y$-direction and there was no orientational offset.</p> <p> </p>
Data for: Chloride-dependent mechanisms of multimodal sensory discrimination and nociceptive sensitization in Drosophila
<p>Individual sensory neurons can be tuned to many stimuli, each driving unique, stimulus-relevant behaviors, and the ability of multimodal nociceptor neurons to discriminate between potentially harmful and innocuous stimuli is broadly important for organismal survival. Moreover, disruptions in the capacity to differentiate between noxious and innocuous stimuli can result in neuropathic pain. <em>Drosophila</em> larval Class III (CIII) neurons are peripheral noxious cold nociceptors and innocuous touch mechanosensors; high levels of activation drive cold-evoked contraction (CT) behavior, while low levels of activation result in a suite of touch-associated behaviors. However, it is unknown what molecular factors underlie CIII multimodality. Here, we show that the TMEM16/anoctamins <em>subdued</em> and <em>white walker</em> (<em>wwk</em>; <em>CG15270</em>) are required for cold-evoked CT, but not for touch-associated behavior, indicating a conserved role for anoctamins in nociception. We also evidence that CIII neurons make use of atypical depolarizing chloride currents to encode cold, and that overexpression of <em>ncc69</em>-a fly homologue of <em>NKCC1</em>-results in phenotypes consistent with neuropathic sensitization, including behavioral sensitization and neuronal hyperexcitability, making <em>Drosophila</em> CIII neurons a candidate system for future studies of the basic mechanisms underlying neuropathic pain</p>
HiCube: Interactive visualization of multiscale and multimodal Hi-C and 3D genome data
<p>Test dataset for HiCube.</p> <p>HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and three-dimensional (3D) genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function.</p> <p>HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube).</p>
Simple Multimodal Algorithmic Reasoning Task Dataset (SMART-101)
<p><strong>Introduction</strong></p> <p>Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, ChatGPT, etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task (and the associated SMART-101 dataset) for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children of younger age (6--8). Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including pattern recognition, algebra, and spatial reasoning, among others. To train deep neural networks, we programmatically augment each puzzle to 2,000 new instances; each instance varied in appearance, associated natural language question, and its solution. To foster research and make progress in the quest for artificial general intelligence, we are publicly releasing our SMART-101 dataset, consisting of the full set of programmatically-generated instances of 101 puzzles and their solutions.</p> <p>The dataset was introduced in our paper <a href="https://arxiv.org/pdf/2212.09993.pdf">Are Deep Neural Networks SMARTer than Second Graders?</a> by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Kevin A. Smith, and Joshua B. Tenenbaum, CVPR 2023</p> <p>Files in the unzipped folder:</p> <ol> <li>./README.md: This Markdown file</li> <li>./SMART101-Data: Folder containing all the puzzle data. See below for details.</li> <li>./puzzle_type_info.csv: Puzzle categorization (into 8 skill classes).</li> </ol> <p><strong>Dataset Organization</strong></p> <p>The dataset consists of `101` folders (numbered from 1-101); each folder corresponds to one distinct puzzle (root puzzle). There are 2000 puzzle instances programmatically created for each root puzzle, numbered from 1-2000. Every root puzzle index (in [1,101]) folder contains: (i) `img/` and (ii) `puzzle_<index>.csv`. The folder `img/` is the location where the puzzle instance images are stored, and `puzzle_<index>.csv` the non-image part of a puzzle. Specifically, a row of `puzzle_<index>.csv` is the following tuple: `<id, Question, image, A, B, C, D, E, Answer>`, where `id` is the puzzle instance id (in [1,2000]), `Question` is the puzzle question associated with the instance, `image` is the name of the image (in `img/` folder) corresponding to this instance `id`, `A, B, C, D, E` are the five answer candidates, and `Answer` is the answer to the question. </p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~12GB. </li> <li>The dataset consists of `101` folders (numbered from 1-101); each folder corresponds to one distinct puzzle (root puzzle). </li> <li>There are 2000 puzzle instances programmatically created for each root puzzle, numbered from 1-2000. </li> <li>Every root puzzle index (in [1,101]) folder contains: (i) `img/` and (ii) `puzzle_<index>.csv`. </li> <li>The folder `img/` is the location where the puzzle instance images are stored, and `puzzle_<index>.csv` contains the non-image part of a puzzle. Specifically, a row of `puzzle_<index>.csv` is the following tuple: `<id, Question, image, A, B, C, D, E, Answer>`, where `id` is the puzzle instance id (in [1,2000]), `Question` is the puzzle question associated with the instance, `image` is the name of the image (in `img/` folder) corresponding to this instance `id`, `A, B, C, D, E` are the five answer candidates, and `Answer` is the correct answer to the question. </li> </ul> <p><strong>Other Details</strong><br> In our paper <a href="https://arxiv.org/pdf/2212.09993.pdf">Are Deep Neural Networks SMARTer than Second Graders?</a>, we provide four different dataset splits for evaluation: (i) Instance Split (IS), (ii) Answer Split (AS), (iii) Puzzle Split (PS), and (iv) Few-shot Split (FS). Below, we provide the details of each split to make fair comparisons to the results reported in our paper. </p> <p><em>Puzzle Split (PS)</em><br> We use the following root puzzle ids as the `Train` and `Test` sets. </p> <table> <thead> <tr> <th scope="col">Split</th> <th scope="col">Root Puzzle Id Sets</th> </tr> </thead> <tbody> <tr> <td>`Test`</td> <td>{ 94,95, 96, 97, 98, 99, 101, 61,62, 65, 66,67, 69, 70, 71,72,73,74,75,76,77}</td> </tr> <tr> <td>`Train`</td> <td>{1,2,...,101} \ Test</td> </tr> </tbody> </table> <p>Evaluation is done on all the `Test` puzzles and their accuracies averaged. For the 'Test' puzzles, we use the instance indices 1701-2000 in the evaluation.</p> <p><em>Few-shot Split (FS)</em></p> <p>We randomly select `k` number of instances from the `Test` sets (that are used in the PS split above) for training in FS split (e.g., `k=100`). These `k` few-shot samples are taken from instance indices 1-1600 of the respective puzzles and evaluation is conducted on all instance ids from 1701-2000.</p> <p><em>Instance Split (IS)</em></p> <p>We split the instances under every root puzzle as: Train = 1-1600, Val = 1601-1700, Test = 1701-2000. We train the neural network models using the `Train` split puzzle instances from all the root puzzles together and evaluate on the `Test` split of all puzzles.</p> <p><em>Answer Split (AS)</em></p> <p>We find the median answer value among all the 2000 instances for every root puzzle and only use this set of the respective instances (with the median answer value) as the `Test` set for evaluation (this set is excluded from the training of the neural networks).</p> <p><em>Puzzle Categorization</em></p> <p>Please see puzzle_type_info.csv for details on the categorization of the puzzles into eight classes, namely (i) counting, (ii) logic, (iii) measure, (iv) spatial, (v) arithmetic, (vi) algebra, (vii) pattern finding, and (viii) path tracing.</p> <p><strong>Other Resources</strong></p> <p>PyTorch code for using the dataset to train deep neural networks is available <a href="https://www.merl.com/publications/TR2023-014">here</a>.</p> <p><strong>Contact</strong><br> Anoop Cherian (cherian@merl.com), Kuan-Chuan Peng (kpeng@merl.com), or Suhas Lohit (slohit@merl.com)</p> <p><br> <strong>Citation</strong><br> If you use the SMART-101 dataset in your research, please cite our paper:</p> <pre><code>@article{cherian2022deep, title={Are Deep Neural Networks SMARTer than Second Graders?}, author={Cherian, Anoop and Peng, Kuan-Chuan and Lohit, Suhas and Smith, Kevin and Tenenbaum, Joshua B}, journal={arXiv preprint arXiv:2212.09993}, year={2022} }</code></pre> <p><strong>Copyright and Licenses</strong></p> <p>The SMART-101 dataset is released under `CC-BY-SA-4.0`.</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2022-2023 SPDX-License-Identifier: CC-BY-SA-4.0 </code></pre> <p> </p>
The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization
<h1>The SIGMA templates and atlases for the Wistar Rat Brain</h1> <p>The current document is a short description of the second version of the SIGMA resources for the Wistar rat brain. For a full description of the resources and the methodologies used to create them please consult the main publication [Barriere D.A. et al 2019].</p> <p>The SIGMA resources are a set of standardized MRI compatible templates and atlases meant to support the analysis of multimodal MRI data of the rat brain. They were developped as part of the SIGMA project, a collaborative project between French (CEA and INSERM) and Portuguese (ICVS) institutions (FCT-ANR/NEU-OSD/0258/2012). They provide a unified and standardized framework for the analysis of multimodal rat brain imaging data, allowing the reporting of results within the coordinate system of the Paxinos-Watson atlas.</p> <p>In this second version, standardized MRI compatible templates have been built from the original acquired data (11.7 Tesla Bruker Scanner at Neuropsin center <a href="https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx" rel="nofollow">https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx</a>) and emulated using the methods developed by Gabriel A. Devenyi (<a href="https://github.com/gdevenyi">https://github.com/gdevenyi</a>) and available here : <a href="https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction">https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction</a>. This pipeline is a re-implementation of the ANTs template construction pipeline requiring ANTs for the primary commands, and running on our cluster facilities using qbatch (<a href="https://islande.hub.inrae.fr/infrastructure" rel="nofollow">https://islande.hub.inrae.fr/infrastructure</a>).</p> <p>Using this methodology we firstly, updated the previous SIGMA spaces (T2sw, T2w, T1w) previously generated using the DARTEL Methods implemented in SPM8 and normalized the whole head images instead of brain.</p> <p>Secondly, we updated the probabilistic maps of the rat brain which are mandatory for the automatic segmentation of the rat brain and standardisation of morphometric analysis. Namely, we created new maps of Grey Matter, White Matter, CSF, Skull and outbrain. Those maps allow the use of SIGMA with the latter release of SPM12, a popular neuroimaging software dedicated to brain imaging analysis but also with ANTs, FSL and AFNI. Additionnally, we revised the Grey Matter/White Matter segmentations since the limits of which (particularly at the thalamic level) were a matter to debate with some users in the previous version of SIGMA.</p> <p>Thirdly, additionnal templates have been created using the optimized ANTs methodology to create from original unpublished data diffusion templates (B0, FA, etc.) at both ex-vivo and in-vivo resolutions.</p> <p>Eventually, using the same strategy, we created a CT/18FDG reference space from data obtained previously [Barrière D.A. et al 2018] which has been normalized with the MRI ex-vivo SIGMA template allowing to the SIGMA resource to propose a multimodal space for CT/TEP/MRI normalisation.</p> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#organisation-of-the-sigma-resources"></a></h2> <h2>Organisation of the SIGMA resources</h2> <p>The SIGMA resources have been organized as four sections : anatomical Imaging, functional imaging, atlases and TEP/CT imaging</p> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#anatomical-imaging"></a></h3> <h3>Anatomical Imaging</h3> <p>In this section a set of templates, priors and brain masks is available for ex-vivo and in-vivo data normalization</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-t2-weighted"></a></h4> <h4>Ex-vivo T2*-weighted</h4> <p>T2*-weighted template + T2*-weighted map + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_csf.nii.gz SIGMA_ExVivo_Anatomical_Brain_gm.nii.gz SIGMA_ExVivo_Anatomical_Brain_mask.nii.gz SIGMA_ExVivo_Anatomical_Brain_out.nii.gz SIGMA_ExVivo_Anatomical_Brain_skull.nii.gz SIGMA_ExVivo_Anatomical_Brain_t2starmap.nii.gz SIGMA_ExVivo_Anatomical_Brain_template.nii.gz SIGMA_ExVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-diffusion"></a></h4> <h4>Ex-vivo diffusion</h4> <p>B0 template + FA template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.25x0.25x0.25mm.</p> <div> <pre><code> SIGMA_ExVivo_Diffusion_Brain_b0.nii.gz SIGMA_ExVivo_Diffusion_Brain_csf.nii.gz SIGMA_ExVivo_Diffusion_Brain_fa.nii.gz SIGMA_ExVivo_Diffusion_Brain_gm.nii.gz SIGMA_ExVivo_Diffusion_Brain_mask.nii.gz SIGMA_ExVivo_Diffusion_Brain_out.nii.gz SIGMA_ExVivo_Diffusion_Brain_skull.nii.gz SIGMA_ExVivo_Diffusion_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-t2-weighted"></a></h4> <h4>In-vivo T2-weighted</h4> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_csf.nii.gz SIGMA_InVivo_Anatomical_Brain_gm.nii.gz SIGMA_InVivo_Anatomical_Brain_mask.nii.gz SIGMA_InVivo_Anatomical_Brain_out.nii.gz SIGMA_InVivo_Anatomical_Brain_skull.nii.gz SIGMA_InVivo_Anatomical_Brain_template.nii.gz SIGMA_InVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-diffusion"></a></h4> <h4>In-vivo diffusion</h4> <p>T2-weighted template + B0 template + FA template + ADC template + brain mask.</p> <p>Spatial resolution 0.375x0.375x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Diffusion_Brain_adc.nii.gz SIGMA_InVivo_Diffusion_Brain_b0.nii.gz SIGMA_InVivo_Diffusion_Brain_fa.nii.gz SIGMA_InVivo_Diffusion_Brain_mask.nii.gz SIGMA_InVivo_Diffusion_Brain_t2.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#functional-imaging"></a></h3> <h3>Functional Imaging</h3> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Functional_Brain_csf.nii.gz SIGMA_InVivo_Functional_Brain_epi.nii.gz SIGMA_InVivo_Functional_Brain_gm.nii.gz SIGMA_InVivo_Functional_Brain_mask.nii.gz SIGMA_InVivo_Functional_Brain_t2.nii.gz SIGMA_InVivo_Functional_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-rat-brain-atlas-version-20--waxholm-atlas-feat-sigma"></a></h3> <h3>SIGMA Rat Brain Atlas Version 2.0 : Waxholm atlas Feat. SIGMA</h3> <p>In this second version of the SIGMA resources we deliver a new SIGMA brain atlas obtained by the normalization of the Waxholm space published by Kleven, H. et al. Nat Methods (2023, <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41592-023-02034-3.pdf" target="_blank" rel="noopener"></a>). The Waxholm rat brain atlas is currently the best numerical 3D atlas of the rat brain. In accordance with authors of this paper we are authorized to modify and embed the WHS atlas within the SIGMA environement to standardize the identification of brain territories. We provide a normalized version the WHS for both ex-vivo and in-vivo of the anatomical SIGMA templates. Finally, we offer linear and non-linear transformations to enable your data to commute between the SIGMA and WHS ex-vivo environments using ANTs commands.</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-atlas"></a></h4> <h4>Ex-vivo atlas</h4> <p>WHS rat brain atlas normalized in ex-vivo T2*-weighted SIGMA template + List of 222 labels created in ITKSnap Format + linear and non-linear transformations for SIGMA-WHS journeys (WHS-to-SIGMA_Transformations folder).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_ExVivo_Anatomical_Brain_Atlas.txt ./WHS-to-SIGMA_Transformations/reference_SIGMA.nii.gz ./WHS-to-SIGMA_Transformations/reference_WHS.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_InverseWarp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_Warp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_02_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_03_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS_SD_rat_atlas_v4.nii.gz ./WHS-to-SIGMA_Transformations/WHS-to-SIGMA_byANTS.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-atlas"></a></h4> <h4>In-vivo atlas</h4> <p>WHS rat brain atlas normalized in in-vivo T2 SIGMA anatomical template + List of 222 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_InVivo_Anatomical_Brain_Atlas.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-brain-meshes"></a></h4> <h4>SIGMA brain meshes</h4> <p>Rat brain mesh created using BrainNet viewers commands in matlab (<a href="https://www.nitrc.org/projects/bnv/" rel="nofollow">https://www.nitrc.org/projects/bnv/</a>).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_Anatomical_Brain_Atlas_mesh.nv </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-functional-atlas"></a></h4> <h4>SIGMA functional atlas</h4> <p>In the original publication of the SIGMA resources, we developed a functional atlas for the rat brain, using a group ICA analysis validated through a RAICAR approach. From this analysis, we identified 59 bilateral ROIs covering cortical, sub-cortical and brainstem structures that are functionally distinct. Despite having been derived from purely functional data, this atlas broadly, if not precisely, correlates with the general anatomical boundaries and many are associated with specific anatomical structures. A primary motivation for the creation of this atlas is derived from the need to perform brain segmentations which is optimized for functional MRI analysis, since the signal sources do not necessarily match typical anatomical boundaries. A similar requirement has been identified by those performing human studies, resulting in efforts to generate more diverse, multi-modal atlases.</p> <p>SIGMA rat brain functional atlas normalized in in-vivo T2 SIGMA functional template + List of 59 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_Functional_Brain_Atlas_Labels.txt SIGMA_Functional_Brain_Atlas_ListOfStructures.csv SIGMA_InVivo_Functional_Brain_Atlas.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-cttep-template"></a></h4> <h4>SIGMA CT/TEP template</h4> <p>In this version of the SIGMA resources we included a CT/TEP template built from the data that previously published (Barriere D.A. et al 2018 , Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5) and acquired on a Triumph™ PET/CT dual modality imaging platform (Gamma Medica, Inc., Northridge, CA, USA), consisting of a LabPET™ avalanche photodiode-based digital PET scanner with a 7.5 cm axial field-of-view capable of achieving an isotropic spatial resolution. A caudal injection of approximately 30 MBq of [18F]-FDG was applied followed by a static acquisition to evaluate [18F]-FDG uptake within brain. CT images were acquired from the high-resolution X-ray computed tomography (CT) modality. Images were reconstructed using the Triumph™ PET/CT software. using the following parameters: 20 iterations, span of 63, field of view of 80 mm with a final matrix resolution of 160 × 160 × 128 and a voxel size of 0.5 × 0.5 × 0.597 mm. Brain dynamic [18F]-FDG images were reconstructed using the same protocol but we generated 32 frames (10 for 5 s, 7 for 10 s, 6 for 30 sec, 6 for 120 s, 2 for 240 s and 1 for 300 s). [18F]-FDG images were reconstructed using 3-D MLEM algorithm providing 0.5 × 0.5 × 0.597 mm images. CT scans were reconstructed using the standard FBP kernel analytical reconstruction algorithms, providing an isotropic image of 512 slices with a final resolution of 0.165 µm isotropic. Both [18F]-FDG and CT data were spatially normalized to the SIGMA ex-vivo template using the previously described methods.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_InVivo_18FDG_Brain_template.nii.gz SIGMA_InVivo_CT_Brain_template.nii.gz </code></pre> <div> </div> </div> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#important-note"></a></h2> <h2>Important Note</h2> <p>SIGMA ressources are provided at the scanner resolution and are oriented in anterior commisure/posterior commisure axis. Center of the images have been set at the anterior commisure level (Bregma 0 mm). Nevertheless, users are invited to increase the resolution of the current images for using in SPM or FSL for accurate coregistration and normalization steps (we recommand x10 increasing). No manipulation of image resolution are required with ANTs. Not tested with AFNI.</p> <p>For any questions regarding the SIGMA ressource, please email the SIGMA Team (<a href="mailto:sigma.preclinical.resources@gmail.com">sigma.preclinical.resources@gmail.com</a>) or Email directly David A. Barrière (<a href="mailto:david.barriere@cnrs.fr">david.barriere@cnrs.fr</a>).</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#references"></a></h1> <h1>REFERENCES</h1> <p>Barrière, D.A. et al. The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization. Nat Commun 10, 5699 (2019). <a href="https://doi.org/10.1038/s41467-019-13575-7" rel="nofollow">https://doi.org/10.1038/s41467-019-13575-7</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41467-019-13575-7.pdf" target="_blank" rel="noopener"></a></p> <p>Kleven, H. et al. Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration. Nat Methods 20, 1822–1829 (2023). <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a></p> <p>Barrière, D.A. et al. Combination of high-fat/high-fructose diet and low-dose streptozotocin to model long-term type-2 diabetes complications. Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5. PMID: 29323186; PMCID: PMC5765114.</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#related-works-using-the-sigma-ressources"></a></h1> <h1>RELATED WORKS USING THE SIGMA RESSOURCES</h1> <p>Grandjean J. et al. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci. 2023 Apr;26(4):673-681. doi: 10.1038/s41593-023-01286-8. Epub 2023 Mar 27. Erratum in: Nat Neurosci. 2023 Jun;26(6):1127-1128. PMID: 36973511; PMCID: PMC10493189.</p> <p>Vidal B. et al. Inter-subject registration and application of the SIGMA rat brain atlas for regional labeling in functional ultrasound imaging. J Neurosci Methods. 2021 May 1;355:109139. doi: 10.1016/j.jneumeth.2021.109139. Epub 2021 Mar 16. PMID: 33741345.</p> <p>Barrière D.A. et al. Paracetamol is a centrally acting analgesic using mechanisms located in the periaqueductal grey. Br J Pharmacol. 2020 Apr;177(8):1773-1792. doi: 10.1111/bph.14934. Epub 2020 Jan 22. PMID: 31734950; PMCID: PMC7070177</p> <p>Barrière D.A. et al. Structural and functional alterations in the retrosplenial cortex following neuropathic pain. Pain. 2019 Oct;160(10):2241-2254. doi: 10.1097/j.pain.0000000000001610. PMID: 31145220.</p> <p>Magalhães, R. et al Resting-State Functional MR Imaging and Spectroscopy Study of the Dorsal Hippocampus in the Chronic Unpredictable Stress Rat Model. J Neurosci. 2019 May 8;39(19):3640-3650. doi: 10.1523/JNEUROSCI.2192-18.2019. Epub 2019 Feb 25. PMID: 30804096; PMCID: PMC6510342.</p> <p>Magalhães, R. et al The dynamics of stress: a longitudinal MRI study of rat brain structure and connectome. Mol Psychiatry. 2018 Oct;23(10):1998-2006. doi: 10.1038/mp.2017.244. Epub 2017 Dec 5. PMID: 29203852.</p>
Multimodal learning of noncoding variant effects using genome sequence and chromatin structure
<p>ncVarPred-1D3D:</p> <p>The data used for testing the inconsistency among genome sequence, epigenetic profile, and later, to show its relation to 3D chromatin structure can be found in sanity_check_data.tar.gz.</p> <p>Some trained model for noncoding mutation effect prediction (mapping genome sequence to epigenetic profile) can be found in CNN_MLP, CNN_GCN, CNN_RNN_MLP, CNN_RNN_GCN.tar.gz.</p> <p>The trained model for pathogenic variants prediction can be found in fewshot_pathogenic_model.tar.gz. </p> <p>The training data can be found in training_data.tar.gz.</p> <p>Some noncoding variant effects prediction results, e.g. eQTL and pathogenic variants, can be replicated using the data shared in ncVar_data.tar.gz.</p>
Experimental Validation video for paper "Safe Multimodal Communication in Human-Robot Collaboration"
<p>Experimental Validatio video for paper Safe Multimodal Communication in Human-Robot Collaboration.</p> <p>The video shows a collaborative manipulator executing a pick-and-place task communication with the user via a multimodal fusion architecture that fuses gesture and voice. The experiment compares a safe and an unsafe interaction and highlight the differences.</p>
A Multimodal Sensing Ring for Quantification of Scratch Intensity
<p>Dataset and code accompanying the paper, A Multimodal Sensing Ring for Quantification of Scratch Intensity. More information located on the paper's <a href="https://github.com/RCHI-Lab/Wearable_Scratch_Intensity/tree/main">github</a> page. For the most updated version of code and data and for a README, please visit the github page. </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.