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6 results for “facial tracking”

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zenodo40/100

EEG and eye-tracking data from a go/no-go saccadic task based on facial expression cues

Electroencephalographic (EEG) and eye-tracking data from 20 healthy individuals who performed a go/no-go saccadic task based on facial expression cues aimed at studying error monitoring processes.   Version 2 includes only the EEG data, but  with triggers of correct and erroneous actions. Version 2 has an error that was corrected for version 3.   EEG Triggers <table> <tbody> <tr> <td> 19 </td> <td> Eye tracker starts recording </td> </tr> <tr> <td> 1 </td> <td> Beginning of each trial </td> </tr> <tr> <td> 200 </td> <td> Gap between neutral and instruction </td> </tr> <tr> <td> 99 </td> <td> Fixation cross between instruction and saccade </td> </tr> <tr> <td> 2 </td> <td> Instruction no-go happy </td> </tr> <tr> <td> 21 </td> <td> Target left no-go happy </td> </tr> <tr> <td> 22 </td> <td> Target right no-go happy </td> </tr> <tr> <td> 121 </td> <td> Response period for no-go happy after target left </td> </tr> <tr> <td> 122 </td> <td> Response period for no-go happy after target right </td> </tr> <tr> <td> 3 </td> <td> Instruction no-go sad </td> </tr> <tr> <td> 31 </td> <td> Target left no-go sad </td> </tr> <tr> <td> 32 </td> <td> Target right no-go sad </td> </tr> <tr> <td> 131 </td> <td> Response period for no-go sad after target left </td> </tr> <tr> <td> 132 </td> <td> Response period for no-go sad after target right </td> </tr> <tr> <td> 4 </td> <td> Instruction pro-right </td> </tr> <tr> <td> 42 </td> <td> Target right pro-right </td> </tr> <tr> <td> 142 </td> <td> Response period for pro-right </td> </tr> <tr> <td> 5 </td> <td> Instruction pro-left </td> </tr> <tr> <td> 51 </td> <td> Target left pro-left </td> </tr> <tr> <td> 151 </td> <td> Response period for pro-left </td> </tr> <tr> <td> 6 </td> <td> Instruction anti-left </td> </tr> <tr> <td> 61 </td> <td> Target left anti-left </td> </tr> <tr> <td> 161 </td> <td> Response period for anti-left </td> </tr> <tr> <td> 7 </td> <td> Instruction anti-right </td> </tr> <tr> <td> 72 </td> <td> Target right anti-right </td> </tr> <tr> <td> 172 </td> <td> Response period for anti-right </td> </tr> <tr> <td> 199 </td> <td> Final fixation period (1.5s black screen after last saccade) </td> </tr> <tr> <td> 190 </td> <td> Eye tracker stops recording </td> </tr> <tr> <td> proCorr </td> <td> Beginning of saccade in the correct direction following a pro-saccade intruction </td> </tr> <tr> <td> proErr </td> <td> Beginning of saccade in the erroneous direction following a pro-saccade intruction </td> </tr> <tr> <td> antiCorr </td> <td> Beginning of saccade in the correct direction following a anti-saccade intruction </td> </tr> <tr> <td> antiErr </td> <td> Beginning of saccade in the erroneous direction following a anti-saccade intruction </td> </tr> <tr> <td> nogoErr </td> <td> Beginning of saccade in the erroneous direction following a no-go intruction </td> </tr> </tbody> </table>

opencc-by-4.0Oct 2021View details →

zenodo40/100

RAVDESS Facial Landmark Tracking

Contact Information If you would like further information about the RAVDESS Facial Landmark Tracking data set, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>.  Tracking Examples Watch a sample of the <a href="https://youtu.be/cxMK2J0P7J0">facial tracking results</a>. Description The <a href="https://affectivedatascience.com/datasets.html#landmark-tracking">RAVDESS Facial Landmark Tracking dataset</a> set contains tracked facial landmark movements from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [<a href="https://zenodo.org/record/1188976">RAVDESS Zenodo page</a>].  Motion tracking of actors' faces was produced by OpenFace 2.1.0 (Baltrusaitis, T., Zadeh, A., Lim, Y. C., & Morency, L. P., 2018).  Tracked information includes: facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation. The Facial Landmark Tracking dataset was created in the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>. This data set contains tracking for all 2452 RAVDESS trials.  All tracking movement data are contained in "FacialTracking_Actors_01-24.zip", which contains 2452 .CSV files.  Each actor has 104 tracked trials (60 speech, 44 song).  Note, there are no song files for Actor 18. Total Tracked Files = (24 Actors x 60 Speech trials) + (23 Actors x 44 Song trials) = 2452 files. Tracking results for each trial are provided as individual comma separated value files (CSV format).  File naming convention of tracked files is identical to that of the RAVDESS.  For example, tracked file "01-01-01-01-01-01-01.csv" corresponds to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4".  For a complete description of the RAVDESS file naming convention and experimental manipulations, please see the <a href="https://zenodo.org/record/1188976">RAVDESS Zenodo page</a>.  Tracking overlay videos for all trials are also provided (720p Xvid, .avi), one zip file per Actor.  As the RAVDESS does not contain "ground truth" facial landmark locations, the overlay videos provide a visual 'sanity check' for researchers to confirm the general accuracy of the tracking results.  The file naming convention of tracking overlay videos also matches that of the RAVDESS.  For example, tracking video "01-01-01-01-01-01-01.avi" corresponds to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4". Tracking File Output Format This data set retained OpenFace's data output format, <a href="https://github.com/TadasBaltrusaitis/OpenFace/wiki/Output-Format">described here in detail</a>.  The resolution of all input videos was 1280x720.  When tracking output units are in pixels, their range of values is (0,0) (top left corner) to (1280,720) (bottom right corner).  Columns 1-3 = Timing and Detection Confidence <ul> <li>1. Frame - The number of the frame (source videos 30 fps), range = 1 to n</li> <li>2. Timestamp - Time of frame, range = 0 to m</li> <li>3. Confidence - Tracker confidence level in current landmark detection estimate, range = 0 to 1</li> </ul> Columns 4-291 = Eye Gaze Detection <ul> <li>4-6. gaze_0_x, gaze_0_y, gaze_0_z - Eye gaze direction vector in world coordinates for eye 0 (normalized), eye 0 is the leftmost eye in the image (think of it as a ray going from the left eye in the image in the direction of the eye gaze).</li> <li>7-9. gaze_1_x, gaze_1_y, gaze_1_z - Eye gaze direction vector in world coordinates for eye 1 (normalized), eye 1 is the rightmost eye in the image (think of it as a ray going from the right eye in the image in the direction of the eye gaze).</li> <li>10-11. gaze_angle_x, gaze_angle_y - Eye gaze direction in radians in world coordinates, averaged for both eyes. If a person is looking left-right this will results in the change of gaze_angle_x (from positive to negative) and, if a person is looking up-down this will result in change of gaze_angle_y (from negative to positive), if a person is looking straight ahead both of the angles will be close to 0 (within measurement error).</li> <li>12-123. eye_lmk_x_0, ..., eye_lmk_x55, eye_lmk_y_0,..., eye_lmk_y_55 - Location of 2D eye region landmarks in pixels. A figure describing the landmark index <a href="https://raw.githubusercontent.com/wiki/TadasBaltrusaitis/OpenFace/images/eye_lmk_markup.png">can be found here</a>.</li> <li>124-291. eye_lmk_X_0, ..., eye_lmk_X55, eye_lmk_Y_0,..., eye_lmk_Y_55,..., eye_lmk_Z_0,..., eye_lmk_Z_55 - Location of 3D eye region landmarks in millimeters. A figure describing the landmark index <a href="https://raw.githubusercontent.com/wiki/TadasBaltrusaitis/OpenFace/images/eye_lmk_markup.png">can be found here</a>.</li> </ul> Columns 292-297 = Head pose <ul> <li>292-294. pose_Tx, pose_Ty, pose_Tz - Location of the head with respect to camera in millimeters (positive Z is away from the camera).</li> <li>295-297. pose_Rx, pose_Ry, pose_Rz - Rotation of the head in radians around X,Y,Z axes with the convention R = Rx * Ry * Rz, left-handed positive sign. This can be seen as pitch (Rx), yaw (Ry), and roll (Rz). The rotation is in world coordinates with the camera being located at the origin.</li> </ul> Columns 298-433 = Facial Landmarks locations in 2D <ul> <li>298-433. x_0, ..., x_67, y_0,...y_67 - Location of 2D landmarks in pixels. A figure describing the landmark index <a href="https://raw.githubusercontent.com/TadasBaltrusaitis/OpenFace/master/imgs/landmark_scheme_68.png">can be found here</a>.</li> </ul> Columns 434-637 = Facial Landmarks locations in 3D <ul> <li>434-637. X_0, ..., X_67, Y_0,..., Y_67, Z_0,..., Z_67 - Location of 3D landmarks in millimetres. A figure describing the landmark index <a href="https://raw.githubusercontent.com/TadasBaltrusaitis/OpenFace/master/imgs/landmark_scheme_68.png">can be found here</a>.  For these values to be accurate, OpenFace needs to have good estimates for fx,fy,cx,cy.</li> </ul> Columns 638-677 = Rigid and non-rigid shape parameters Parameters of a point distribution model (PDM) that describe the rigid face shape (location, scale and rotation) and non-rigid face shape (deformation due to expression and identity). For more details, please refer to chapter 4.2 of my Tadas Baltrusaitis's PhD thesis [<a href="https://www.repository.cam.ac.uk/handle/1810/245253">download link</a>]. <ul> <li>638-643. p_scale, p_rx, p_ry, p_rz, p_tx, p_ty - Scale, rotation, and translation terms of the PDM.</li> <li>644-677. p_0, ..., p_33 - Non-rigid shape parameters.</li> </ul> Columns 687-712 = Facial Action Units Facial Action Units (AUs) are a way to describe human facial movements (Ekman, Friesen, and Hager, 2002) [<a href="https://en.wikipedia.org/wiki/Facial_Action_Coding_System">wiki link</a>].  More information on OpenFace's implementation of AUs <a href="https://github.com/TadasBaltrusaitis/OpenFace/wiki/Action-Units">can be found here</a>. <ul> <li>678-694. AU01_r, AU02_r, AU04_r, AU05_r, AU06_r, AU07_r, AU09_r, AU10_r, AU12_r, AU14_r, AU15_r, AU17_r, AU20_r, AU23_r, AU25_r, AU26_r, AU45_r - Intensity of AU movement, range from 0 (no muscle  contraction) to 5 (maximal muscle contraction).</li> <li>695-712. AU01_c, AU02_c, AU04_c, AU05_c, AU06_c, AU07_c, AU09_c, AU10_c, AU12_c, AU14_c, AU15_c, AU17_c, AU20_c, AU23_c, AU25_c, AU26_c, AU28_c, AU45_c - Presence or absence of 18 AUs, range 0 (absent, not detected) to 1 (present, detected).</li> </ul> Note, OpenFace's columns 2 and 5 (face_id and success, respectively) were not included in this data set. These values were redundant as a single face was detected in all frames, in all 2452 trials. Tracking Overlay Videos Tracking overlay videos visualize most aspects of the tracking output described above. <ul> <li>Frame - Column 1, Top left corner of video</li> <li>Eye Gaze - Columns 4-11. Indicated by green ray emanating from left and right eyes.</li> <li>Eye region landmarks 2D - Columns 12-123. Red landmarks around left and right eyes, and black circles surrounding left and right irises.</li> <li>Head pose - Columns 292-297. Blue bounding box surrounding the actor's head.</li> <li>Facial landmarks 2D - Columns 298-433. Red landmarks on the participant's left and right eyebrows, nose, lips, and jaw.</li> <li>Facial Action Unit Intensity - Columns 687-694. All 17 AUs are listed on the left side of the video in black text.  Intensity level (0-5) of each AU is indicated by the numeric value and blue bar.</li> <li>Facial Action Unit Presence - Columns 695-712. All 18 AUs are listed on the right side of the video in black & green text.  Absence of an AU (0) is in black text with the numeric value 0.0. Presence of an AU (1) is in green text with the numeric value 1.0.</li> </ul> Camera Parameters and 3D Calibration Procedure This data set contains accurate estimates of actors' 3D head poses. To produce these, camera parameters at the time of recording were required (distance from camera to actor, and camera field of view).  These values were used with OpenCV's camera calibration procedure, <a href="https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html">described here</a>, to produce estimates of the camera's focal length and optical center at the time of actor recordings.  The four values produced by the calibration procedure (fx,fy,cx,cy) were input to OpenFace as command line arguments during facial tracking, <a href="https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments">described here</a>, to produce accurate estimates of 3D head pose. Camera Parameters <ul> <li>Distance between camera and actor = 1.4 meters</li> <li>Camera field of view = 0.5 meters</li> <li>Focal length in x (fx) = 6385.9</li> <li>Focal length in y (fy) = 6339.6</li> <li>Optical center in x (cx) = 824.241</li> <li>Optical center in y (cy) = 1033.6</li> </ul> The use of OpenCV's calibration procedure was required as the video camera used in the RAVDESS recordings did not report focal length values.  Unlike SLR cameras, most video cameras do not provide this information to the user due to their dynamic focus feature.  For all RAVDESS recordings, camera distance, field of view, and focal point (manual fixed camera focus) were kept constant. License information The RAVDESS Facial Landmark Tracking data set is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>. How to cite the RAVDESS Facial Tracking data set Academic citation  If you use the RAVDESS Facial Tracking data set in an academic publication, please cite both references:  <ol> <li>Swanson, R., Livingstone, SR., & Russo, FA. (2019). RAVDESS Facial Landmark Tracking (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3255102</li> <li>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</li> </ol> All other attributions  If you use the RAVDESS in a form other than an academic publication, such as in a blog post, school project, or non-commercial product, please use the following attribution: "<a href="https://zenodo.org/record/3255102">RAVDESS Facial Landmark Tracking</a>" by Swanson, Livingstone, & Russo is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>. Related Data sets <ul> <li>The Ryerson Audio-Visual Database of Emotional Speech and Song [<a href="https://zenodo.org/record/1188976">Zenodo project page</a>].</li> </ul>

opencc-by-nc-sa-4.0Jun 2019View details →

zenodo40/100

Facial Expression and Landmark Tracking (FELT) dataset

Contact Information If you would like further information about the Facial expression and landmark tracking data set, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>.  Facial Expression examples Watch a sample of the <a href="https://youtu.be/OA5gZ6iycPc">facial expression tracking results</a>. Commercial Licenses Commercial licenses for this dataset can be purchased.  For more information, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com</a>. Description The <a href="https://affectivedatascience.com/datasets.html#felt">Facial Expression and Landmark Tracking (FELT) dataset</a> dataset contains tracked facial expression movements and animated videos from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [<a href="../record/1188976">RAVDESS Zenodo page</a>]. Tracking data and videos were produced by Py-Feat 0.6.2 (2024-03-29 release) (Cheong, J.H., Jolly, E., Xie, T. et al. Py-Feat: Python Facial Expression Analysis Toolbox. Affec Sci 4, 781–796 (2023). https://doi.org/10.1007/s42761-023-00191-4) and custom code (<a href="https://github.com/harveyliao/Py-feat-RAVDESS">github repo</a>). Tracked information includes: facial emotion classification estimates, facial landmark detection (68 points), head pose estimation (yaw, pitch, roll, x, y), and facial Action Unit (AU) recognition. Videos include: landmark overlay videos, AU activation animations, and landmark plot animations. The FELT dataset was created at the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>. This dataset contains tracking data and videos for all 2452 RAVDESS trials. Raw and smoothed tracking data are provided. All tracking movement data are contained in the following archives: raw_motion_speech.zip, smoothed_motion_speech.zip, raw_motion_song.zip, and smoothed_motion_song.zip. Each actor has 104 tracked trials (60 speech, 44 song).  Note, there are no song files for Actor 18. Total Tracked Files = (24 Actors x 60 Speech trials) + (23 Actors x 44 Song trials) = 2452 CSV files. Tracking results for each trial are provided as individual comma separated value files (CSV format).  File naming convention of raw and smoothed tracked files is identical to that of the RAVDESS.  For example, smoothed tracked file "01-01-01-01-01-01-01.csv" corresponds to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4".  For a complete description of the RAVDESS file naming convention and experimental manipulations, please see the <a href="../record/1188976">RAVDESS Zenodo page</a>.  Landmark overlays, AU activation, and landmark plot videos for all trials are also provided (720p h264, .mp4). Landmark overlays present tracked landmarks and head pose overlaid on the original RAVDESS actor video. As the RAVDESS does not contain "ground truth" facial landmark locations, the overlay videos provide a visual 'sanity check' for researchers to confirm the general accuracy of the tracking results. Landmark plot animations present landmarks only, anchored to the top left corner of the head bounding box with translational head motion removed. AU activation animations visualize intensity of AU activations (0-1 normalized) as a heatmap over time. The file naming convention of all videos also matches that of the RAVDESS.  For example, "Landmark_Overlay/01-01-01-01-01-01-01.mp4", "Landmark_Plot/01-01-01-01-01-01-01.mp4", "ActionUnit_Animation/01-01-01-01-01-01-01.mp4", all correspond to RAVDESS audio-video file "01-01-01-01-01-01-01.mp4". Smoothing procedure Raw tracking data were first low-pass filtered with a 5th order butterworth filter (cutoff_freq = 6, sampling_freq = 29.97, order = 5) to remove high-frequency noise. Data were then smoothed with a Savitzky-Golay filter (window_length = 11, poly_order = 5). Scipy.signal (v 1.13.1) was used for both procedures. Landmark Tracking models Six separate machine learning models were used by Py-Feat to perform various aspects of tracking and classification. Video outputs generated by different combinations of ML models were visually compared, with final model choice determined by voting of first and second authors. Models were specified in the call to <code>Detector</code> class (<a href="https://py-feat.org/basic_tutorials/02_detector_imgs.html?highlight=facescore">described here</a>). Exact function call as follows: <code>    Detector(face_model='img2pose',</code> <code>    landmark_model='mobilenet',</code> <code>    au_model='xgb',</code> <code>    emotion_model='resmasknet',</code> <code>    facepose_model='img2pose-c',</code> <code>    identity_model='facenet',</code> <code>    device='cuda',</code> <code>    n_jobs=1,</code> <code>    verbose=False,</code> <code>    )</code> Default Py_feat parameters to each model were used in most cases. Non-defaults were specified in the call to <code>detect_video</code> function (<a href="https://py-feat.org/pages/api.html?highlight=detect_video">described here</a>). Exact function call as follows: <code>    (video_path,</code> <code>    skip_frames=None,</code> <code>    output_size=(720, 1280),</code> <code>    batch_size=5,</code> <code>    num_workers=0,</code> <code>    pin_memory=False,</code> <code>    face_detection_threshold=0.83,</code> <code>    face_identity_threshold=0.8</code> <code>    )</code> Tracking File Output Format This data set retained Py-Feat's data output format. The resolution of all input videos was 1280x720.  Tracking output units are in pixels, their range of values is (0,0) (top left corner) to (1280,720) (bottom right corner).  Column 1 = Timing information <ul> <li>1. frame - The number of the frame (source videos 29.97 fps), range = 1 to n</li> </ul> Columns 2-5 = Head bounding box <ul> <li>2-3. FaceRectX, FaceRectY - X and Y coordinates of top-left corner of head bounding box (pixels)</li> <li>4-5. FaceRectWidth, FaceRectHeightF - Width and Height of head bounding box (pixels)</li> </ul> Column 6 = Face detection confidence <ul> <li>FaceScore - Confidence level that a human face was deteceted, range = 0 to 1</li> </ul> Columns 7-142 = Facial landmark locations in 2D <ul> <li>7-142. x_0, ..., x_67, y_0,...y_67 - Location of 2D landmarks in pixels. A figure describing the landmark index <a href="https://raw.githubusercontent.com/TadasBaltrusaitis/OpenFace/master/imgs/landmark_scheme_68.png">can be found here</a>.</li> </ul> Columns 143-145 = Head pose <ul> <li>143-145. Pitch, Roll, Yaw - Rotation of the head in degrees (<a href="https://py-feat.org/basic_tutorials/02_detector_imgs.html?highlight=facerect#:~:text=single_face_prediction.poses%20%23%20(in%20degrees)">described here</a>). The rotation is in world coordinates with the camera being located at the origin.</li> </ul> Columns 146-165 = Facial Action Units Facial Action Units (AUs) are a way to describe human facial movements (Ekman, Friesen, and Hager, 2002) [<a href="https://en.wikipedia.org/wiki/Facial_Action_Coding_System">wiki link</a>].  More information on Py-Feat's implementation of AUs <a href="https://py-feat.org/pages/au_reference.html">can be found here</a>. <ul> <li>145-150, 152-153, 155-158, 160-165. AU01, AU02, AU04, AU05, AU06, AU09, AU10, AU12, AU14, AU15, AU17, AU23, AU24, AU25, AU26, AU28, AU43 - Intensity of AU movement, range from 0 (no muscle  contraction) to 1 (maximal muscle contraction).</li> <li>151, 154, 159. AU07, AU11, AU20 - Presence or absence of AUs, range 0 (absent, not detected) to 1 (present, detected).</li> </ul> Columns 166-172 = Emotion classification confidence <ul> <li>162-172. anger, disgust, fear, happiness, sadness, surprise, neutral - Confidence of classified emotion category, range 0 (0%) to 1 (100%) confidence.</li> </ul> Columns 173-685 = Face identity score Identity of faces contained in the video were classified using the FaceNet model (<a href="https://arxiv.org/pdf/1503.03832">described here</a>). This procedure generates at 512 dimension Euclidean embedding space. <ul> <li>173. Identity - Predicated individual identifyed in the RAVDESS video. Note, value is always Person_0, as each video only contains a single actor at all times (categorical).</li> <li>174-685. Identity_1, ..., Identity_512 - Face embedding vector used by FaceNet to perform facial identity matching.</li> </ul> Column 686 = Input video <ul> <li>686. frame - The number of the frame (source videos 29.97 fps), range = 1 to n</li> </ul> Columns 687-688 = Timing information <ul> <li>687. frame.1 - The number of the frame (source videos 29.97 fps), duplicated column, range = 1 to n</li> <li>688. approx_time - Approximate time of current frame (0.0 to x.x, in seconds)</li> </ul> Tracking videos Landmark Overlay and Landmark Plot videos were produced with <code>plot_detections</code> function call (<a href="https://py-feat.org/pages/api.html?highlight=plot_detections#feat.data.Fex.plot_detections">described here</a>). This function generated invidual images for each frame, which were then compiled into a video using the imageio library (<a href="https://pypi.org/project/imageio/">described here</a>). AU Activation videos were produced with plot_face function call (<a href="https://py-feat.org/pages/api.html?highlight=plot_face#feat.plotting.plot_face">described here</a>). This function also generated invidual images for each frame, which were then compiled into a video using the imageio library. Some frames could not be correctly generated by Py-Feat, producing only the AU heatmap but failing to plot/locate facial landmarks. These frames were dropped prior to compositing the output video. Drop rate was approximately 10% of all frames, in each video. Dropped frames were distributed evenly across the video timeline (i.e. no apparent clustering). License information The RAVDESS Facial expression and landmark tracking data set is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>. How to cite the RAVDESS Facial Tracking data set Academic citation  If you use the RAVDESS Facial Tracking data set in an academic publication, please cite both references:  <ol> <li>Liao, Z., Livingstone, SR., & Russo, FA. (2024). RAVDESS Facial expression and landmark tracking (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.13243600</li> <li>Livingstone SR, Russo FA (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</li> </ol> All other attributions  If you use the RAVDESS Facial expression and landmark tracking dataset in a form other than an academic publication, such as in a blog post, data science project or competition, school project, or non-commercial product, please use the following attribution: "<a href="../records/13243600">RAVDESS Facial expression and landmark tracking</a>" by Liao, Livingstone, & Russo is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>. Related Data sets <ul> <li>The Ryerson Audio-Visual Database of Emotional Speech and Song [<a href="../record/1188976">Zenodo project page</a>].</li> </ul>

openAug 2024View details →

ClinicalTrials.gov24/100

Effect of Release of Upper Track of Deep Front Facial Line Versus Myofascial Release With IASTM on Patients With Upper Cross Syndrome

ClinicalTrials.gov study NCT06907459. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov24/100

Facial Skin Health Tracking Feasibility in Breast Cancer Patients

ClinicalTrials.gov study NCT04035408. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov24/100

Eye-Tracking FP "A Pilot Study of the Quantitative Evaluation of the Attention Paid to Faces With Facial Palsy by the Eye-tracking Technology.

ClinicalTrials.gov study NCT04886245. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record