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191 results for “Track dataset”
Mediterranean Cyclone tracks between 1979-2018 (40 years) from a high-resolution perspective using ECMWF ERA5 dataset
<p>The present dataset presents the trajectories of the 13,157 cyclones identified within the Mediterranean Region (MR) between 1979 and 2018 (40 years). These cyclone tracks were obtained using the new Cyclone Detection and Tracking Method (CDTM) described in Aragão e Porcù (2021) to take advantage of the recent availability of a high-resolution reanalysis dataset of ECMWF ERA5. The CDTM uses hourly data of Geopotential Height at 1000 hPa with a spatial resolution of 0.25°x0.25°, and the analysis' domain covers the area within 15°W to 48° E and 21° N to 54°N. Additionally, trying to eliminate artificial low-pressure cores, short-living thermal-lows or too weak cyclones as much as possible, the present study only considered cyclones lasting more than 24h.<br> The dataset presents hourly information for all cyclones from the cyclogenesis time to the cyclolysis time. Each record presents: [1] Cyclone ID (integer, 8 digits), [2] Cyclone centre longitude position (°E, real, 8 digits, 3 decimal digits), [3] Cyclone centre latitude position (°N, real, 8 digits, 3 decimal digits), [4] Year (integer, 4 digits), [5] Month (integer, 2 digits), [6] Day (integer, 2 digits), [7] Hour (integer, 2 digits), [9] Cyclone centre Geopotential Height at 1000 hPa (m, real, 9 digits, 3 decimal digits).<br> The analyses presented in Aragão e Porcù (2021) revealed that the proposed CDTM is capable to capture almost the totality of the observed cyclones, as well as describing its respective area of cyclogenesis, trajectories, and durations. More than an adaptation to a high-resolution dataset, the method brings as its primary contribution a suitable set of parameters to systematically identify and track the cyclonic activities in the Mediterranean, where cyclones do not have sizeable horizontal pressure gradients and present a shorter lifetime compared to open-ocean cyclones.</p> <p>Cite this article</p> <p>Aragão, L., Porcù, F. Cyclonic activity in the Mediterranean region from a high-resolution perspective using ECMWF ERA5 dataset. <em>Clim Dyn</em> (2021). https://doi.org/10.1007/s00382-021-05963-x</p>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Dataset: Does vendor breeding colony influence sign- and goal-tracking in Pavlovian conditioned approach?
<p>Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.</p>
TokTrack: A Complete Token Provenance and Change Tracking Dataset for the English Wikipedia
<p><strong>Fixes in version 1.1 (= Zenodo's "version 2")</strong></p> <p>*In 20161101-revisions-part1-12-1728.csv, missing first data line is added.</p> <p>*In Current_content and Deleted_content files, some token values ('str' column) which contain regular quotes ('"') are fixed.</p> <p>*In Current_content and Deleted_content files, some wrong revision ID values for 'origin_rev_id', 'in' and 'out' columns are fixed.</p> <p> ------</p> <p><strong>This dataset contains every instance of all tokens (≈ words) ever written in undeleted, non-redirect English Wikipedia articles until October 2016, in total 13,545,349,787 instances. Each token is annotated with (i) the article revision it was originally created in, and (ii) lists with all the revisions in which the token was ever deleted and (potentially) re-added and re-deleted from its article, enabling a complete and straightforward tracking of its history.</strong></p> <p>This data would be exceedingly hard to create by an average potential user as it is (i) very expensive to compute and as (ii) accurately tracking the history of each token in revisioned documents is a non-trivial task. <br> Adapting a state-of-the-art algorithm, we have produced a dataset that allows for a range of analyses and metrics, already popular in research and going beyond, to be generated on complete-Wikipedia scale; ensuring quality and allowing researchers to forego expensive text-comparison computation, which so far has hindered scalable usage.</p> <p>This dataset, its creation process and use cases are described in a dedicated dataset paper of the same name, published at the ICWSM 2017 conference. In this paper, we show how this data enables, on token level, computation of provenance, measuring survival of content over time, very detailed conflict metrics, and fine-grained interactions of editors like partial reverts, re-additions and other metrics.</p> <p>Tokenization used: https://gist.github.com/faflo/3f5f30b1224c38b1836d63fa05d1ac94</p> <p>Toy example for how the token metadata is generated: <br> https://gist.github.com/faflo/8bd212e81e594676f8d002b175b79de8</p> <p><strong>Be sure to read the ReadMe.txt or - even more detailed - the supporting paper which is referenced under "related identifiers".</strong></p>
Research Data Alliance Interest Group Professionalising Data Stewardship Career Tracks Survey Dataset
<p>This is the final dataset resulting from the data steward Career Tracks survey that the Reseach Data Alliance (RDA) Interest Group Professionalising Data Stewardship carried out in 2022. Data stewards were defined as professionals who aim at guaranteeing that data is appropriately treated in all stages of the research cycle (i.e., design, collection, processing, analysis, preservation, data sharing and reuse); we invited responses from participants who either now or in the past carried out data stewardship functions, regardless of their job title. The survey asked respondents about their job titles, the organizational context in which they work(ed) including contract types and domains, their educational background, and how they perceive their professional future. </p><p>This dataset publication includes:</p><ol><li>Survey response data in CSV format. The file includes data from 241 respondents who consented to participate in the survey and share the data via a repsoitory, who indicated that they either currently work or have worked in the past in a data stewardship role, and who responded to at least one further question.</li><li>Thematic analysis of the qualitative questions Q11 and Q12 in PDF format.</li></ol>
Dataset for wave-by-wave particle tracking in the surf zone
<p>This dataset comprises 49 trajectories with 3D positions of buoyant tracers reconstructed from stereo camera imaging using two cameras and a standard triangulation process. The data is extracted from stereo image frames of the sea surface, captured at a rate of 30 frames per second. These images were collected between 15:13:00 and 17:18:59 UTC on September 7, 2019, near the island of Sylt, Germany.</p> <div>An appropriate coordinate system was used to better represent the tracer position time series for the analysis of tracers position, velocity and acceleration. </div> <div>Details about the coordinate system are provided in the associated manuscript and supporting information as well as in </div> <div><a title="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" target="_blank" rel="noopener noreferrer">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722</a></div> <div> </div> <div>The dataset is organized into four columns: the frame time [µs]; the X coordinate [m], defining the horizontal position with the origin at the base of Pole 2 (see above referenced paper) and oriented shoreward; the Y coordinate [m], denoting the transverse position perpendicular to the direction of wave propagation ; </div> <div>and the Z coordinate [m], specifying the vertical position with the axis oriented upward.</div> <p>These coordinates were obtained using a triangulation algorithm and adjusted using a coordinate system transformation to yield a precise, physically meaningful representation of the trajectories. The data is provided in .mat (MATLAB) format</p>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
Dataset of "Tracking high-valent surface iron species in the oxygen evolution reaction on cobalt iron (oxy)hydroxides"
<p>Dataset of the paper entitled "Tracking high-valent surface iron species in the oxygen evolution reaction on cobalt iron (oxy)hydroxides"</p>
Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories</p> <p>- The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file.</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
X-ray micro-computed tomography based particle tracking velocimetry dataset in a sandpack
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a sand pack (grainsize 500-710 µm; sample size 4 mm diameter by 2 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 79 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2021 Competition.</p> <p><strong>Contents:</strong></p> <ul> <li>IPIN2021_Track03_TechnicalAnnex_V1-02.pdf: Technical annex describing the competition</li> <li>01-Logfiles: This folder contains a subfolder with the 105 training logfiles, 80 of them single floor indoors, 10 in outdoor areas, 10 of them in the indoor auditorium with floor-trasitio and 5 of them in floor-transition zones, a subfolder with the 20 validation logfiles, and a subfolder with the 3 blind evaluation logfile as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 82 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results.</li> </ul> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.5948678</li> </ul>
Open Satellite Video Single Target Tracking Datasets (OpenSatSTTD)
<p>We collect the latest open-source datasets for satellite video single target tracking (SatSTT) and launch the OpenSatSTTD project to promote the sharing of the latest research datasets in the SatSTT field. Satellite videos in the OpenSatSTTD project are collected from different sensors and platforms, and four targets (i.e., vehicles, trains, airplanes and vessels) are annotated by oriented bounding boxes. Users can obtain all satellite videos in the OpenSatSTTD project from links in the files.</p> <p>Source:</p> <p>Zheng, Ying., Zhu, Q., Luo, J., Li, Z., Lin, Z., Huang, X., and Zhang L.: Single Target Tracking in High-Resolution Satellite Videos: A Comprehensive Review (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6780820, 2022.</p> <p> </p>
Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.
<p><strong>This database provides as polygons the contours of 900 juniper shrubs (<em>Juniperus communis L.</em> and <em>Juniperus sabina L.</em>) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation). </strong></p>
Container spreader pose tracking dataset
<p>This dataset contains image sequences that feature a moving quay crane spreader in a port environment while unloading a container cargo vessel. A container crane spreader is a device that is installed on a crane and used to lift containers after attaching onto them.</p> <p><br> The sequences were acquired from a viewpoint similar to that of the crane operator using a camera installed next to the operator’s cabin at a height of approximately 20 meters above the quay. The camera thus moves with the crane, resulting in a non-stationary image background.</p> <p>The dataset is organized into several RAR archives, one for each sequence. In addition to the undistorted image frames, it includes for every sequence a text file whose each line consists of the frame id for every image, the spreader’s bounding box and the spreader’s 6D pose (Rodrigues vector for the orientation, and the translation vector). The axis-aligned 2D bounding box is in the format <em>x0 y0 w h</em> where <em>(x0, y0)</em> is the top left corner and <em>w x h</em> its size, all in pixels. The spreader’s pose is defined with respect to the camera coordinate frame. Also included are the camera intrinsics matrix K for each sequence along with a common 3D mesh model for the spreader.</p> <p>The spreader’s mesh model is supplied in PLY format. For a certain image frame, a model vertex M transforms to the camera coordinate system as R*M + t, R and t being the spreader’s pose (R is the equivalent rotation matrix). The homogeneous coordinates of that vertex’s projection on the image frame are K*(R*M + t).</p> <p><br> The dataset can support research on topics such as object localization, object detection, pose estimation, tracking, etc.<br> If you use this dataset in your research work, you are kindly asked to cite the following paper in your publications:</p> <p>M. Lourakis and M. Pateraki, "<em>Markerless Visual Tracking of a Container Crane Spreader,</em>" 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 2579-2586, doi: <a href="https://doi.org/10.1109/ICCVW54120.2021.00291">10.1109/ICCVW54120.2021.00291</a>.</p>
The INI-30 Dataset : Event Camera for Eye Tracking
<p>The Ini-30 dataset is collected with two event cameras mounted on a glass frame. Each DVXplorer sensor (640 × 480 pixels) is attached on the side of the frame. The power supply was provided via a 2 meter cable connected from the cameras to a computer, which provided enough freedom of movement. Differently from [2, 24], the participants were not instructed to follow a dot on a screen, but rather encouraged to look around to collect natural eye movements. As shown in Fig. 1, the event cameras were securely screwed on a 3D-printed case attached to the side of the glass frame. The data was annotated based on accumulated linearly decayed events by defining the pixel intensity as function of the linear accumulation of previous pixel intensity. Next we labeled the position of the pupil in the DVS’s array manually, using an assistive labeling tool. We discarded the first 20ms of events to ensure the eye was visible and annotations met the level of image-based annotators. The number of labels per recording was intentionally variable, spanning from 475 to 1’848 with a time per label ranging from 20.0 to 235.77 milliseconds depending on the overall duration of the sample. This setup allows for unconstrained head movements, enables to capture event data from eye movement in a ”in-the-wild” setting and allows the generation of a representative, unique, diverse and challenging dataset.<br><br>NOTE : the annotations relates to the ellipse of the pupil on the image</p>
Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2017 Competition (Sapporo, Japan).</p> <p><strong>Contents:</strong></p> <ol> <li>Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.</li> <li>Track3_TechnicalAnnex.pdf: Technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 25 training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 7 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/Octave parser, the raster maps, the visualization of the training routes and the location of the BLE beacon (CAR) and some Wi-Fi APs (UJIUB).</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 505 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; Jiménez, A. R.; Moreira, A.; Lungenstrass, T.; Lu, W.-C.; Knauth, S.; Mendoza-Silva, G.M.; Seco, F.; Perez-Navarro, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Farina, J.; Morales, J.P.; Lu, W.-C.; Cheng, H.-T.; Yang, S.-S.; Fang, S.-H.; Chien, Y.-R. and Tsao, Y. Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition Sensors Vol. 18(2), 2018. <a href="http://dx.doi.org/10.3390/s18020487">http://dx.doi.org/10.3390/s18020487</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823924">http://dx.doi.org/10.5281/zenodo.2823924</a> </li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2017/2017-competition-home">http://evaal.aaloa.org/2017/2017-competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2017track3/">http://indoorloc.uji.es/ipin2017track3/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul> <p><br> </p>
Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).</p> <p><strong>Contents:</strong></p> <ol> <li>IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 22 training logfiles, a subfolder with the 15 (13 + 2) validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> <li>03-Evaluation_alternative: This folder contains the alternative scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. This version is compatible with MatLab and Octave and does not require any toolbox. In some cases, the differences in the reported errors might be around 10 cm with respect to the script used in the competition. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Renaudin, V.; Ortiz, M.; Perul, J.; Torres-Sospedra, J.; Ramón Jimenez, A.; Pérez-Navarro, A.; Martín Mendoza-Silva, G.; Seco, F.; Landau, Y.; Marbel, R.; Ben-Moshe, B.; Zheng, X.; Ye, F.; Kuang, J.; Li, Y.; Niu, X.; Landa, V.; Hacohen, S.; Shvalb, N.; Lu, C.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.; Ding, Z.; Xu, F.; Kronenwett, N.; Vladimirov, B.; Lee, S.; Cho, E.; Jun, S.; Lee, C.; Park, S.; Lee, Y.; Rew, J.; Park, C.; Jeong, H.; Han, J.; Lee, K.; Zhang, W.; Li, X.; Wei, D.; Zhang, Y.; Park, S. Y.; Park, C. G.; Knauth, S.; Pipelidis, G.; Tsiamitros, N.; Lungenstrass, T.; Pablo Morales, J.; Trogh, J.; Plets, D.; Opiela, M.; Shih-Hau Fang Tsao, Y.; Chien, Y.-R.; Yang, S.-S.; Ye, S.-J.; Ali, M. U.; Hur, S.; and Park, Y. Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition IEEE Access Vol. 7, pp. 148594-148628, 2019. http://dx.doi.org/10.1109/ACCESS.2019.2944389</li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2018/call-for-competitions">http://evaal.aaloa.org/2018/call-for-competitions</a></li> <li><a href="http://ipin-conference.org/2018/ipincompetition/">http://ipin-conference.org/2018/ipincompetition/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul>
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
<p>Version 4 of the dataset is available (Sep 19 2019)!</p> <p>Note this version has significantly more data than Version 2. </p> <p>Dataset description paper (full version) is available!</p> <p>https://arxiv.org/pdf/1903.06754.pdf (updated Sep 7 2019)</p> <p>Tools for visualizing the data is available!</p> <p>https://github.com/corgiTrax/Gaze-Data-Processor</p> <p> </p> <p><strong>=========================== Dataset Description ===========================</strong></p> <p>We provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded.</p> <p> </p> <p>Q & A: Why frame-by-frame game mode?</p> <p><strong>Resolving state-action mismatch</strong>: Closed-loop human visuomotor reaction time is around 250-300 milliseconds. Therefore, during gameplay, state (image) and action that are simultaneously recorded at time step t could be mismatched. Action at time t could be intended for a state 250-300ms ago. This effect causes a serious issue for supervised learning algorithms, since label at and input st are no longer matched. Frame-by-frame game play ensures states and actions are matched at every timestep.</p> <p><strong>Maximizing human performance</strong>: Frame-by-frame mode makes gameplay more relaxing and reduces fatigue, which could normally result in blinking and would corrupt eye-tracking data. More importantly, this design reduces sub-optimal decisions caused by inattentive blindness.</p> <p><strong>Highlighting critical states that require multiple eye movements</strong>: Human decision time and all eye movements were recorded at every frame. The states that could lead to a large reward or penalty, or the ones that require sophisticated planning, will take longer and require multiple eye movements for the player to make a decision. Stopping gameplay means that the observer can use eye-movements to resolve complex situations. This is important because if the algorithm is going to learn from eye-movements it must contain all “relevant” eye-movements.</p> <p> </p> <p><strong>============================ Readme ============================</strong></p> <p>1. meta_data.csv: meta data for the dataset., including:</p> <ul> <li> <p>GameName: String. Game name. e.g., “alien” indicates the trial is collected for game Alien (15 min time limit). “alien_highscore” is the trajectory collected from the best player’s highest score (2 hour limit). See dataset description paper for details.</p> </li> </ul> <ul> <li> <p>trial_id: Integer. One can use this number to locate the associated .tar.bz2 file and label file.</p> </li> <li> <p>subject_id: Char. Human subject identifiers.</p> </li> <li> <p>load_trial: Integer. 0 indicates that the game starts from scratch. If this field is non-zero, it means that the current trial continues from a saved trial. The number indicates the trial number to look for.</p> </li> <li> <p>highest_score: Integer. The highest game score obtained from this trial.</p> </li> <li> <p>total_frame: Number of image frames in the .tar.bz2 repository.</p> </li> <li> <p>total_game_play_time: Integer. game time in ms. </p> </li> <li> <p>total_episode: Integer. number of episodes in the current trial. An episode terminates when all lives are consumed.</p> </li> <li> <p>avg_error: Float. Average eye-tracking validation error at the end of each trial in visual degree (1 visual degree = 1.44 cm in our experiment). See our paper for the calibration/validation process.</p> </li> <li> <p>max_error: Float. Max eye-tracking validation error. </p> </li> <li> <p>low_sample_rate: Percentage. Percentage of frames with less than 10 gaze samples. The most common reason for this is blinking.</p> </li> <li> <p>frame_averaging: Boolean. The game engine allows one to turn this on or off. When turning on (TRUE), two consecutive frames are averaged, this alleviates screen flickering in some games.</p> </li> <li> <p>fps: Integer. Frame per second when an action key is held down.</p> </li> </ul> <p> </p> <p>2. [game_name].zip files: these include data for each game, including:</p> <p>*.tar.bz2 files: contains game image frames. The filename indicates its trial number.</p> <p>*.txt files: label file for each trial, including:</p> <ul> <li> <p>frame_id: String. The ID of a frame, can be used to locate the corresponding image frame in .tar.bz2 file.</p> </li> <li> <p>episode_id: Integer (not available for some trials). Episode number, starting from 0 for each trial. A trial could contain a single trial or multiple trials.</p> </li> <li> <p>score: Integer (not available for some trials). Current game score for that frame.</p> </li> <li> <p>duration(ms): Integer. Time elapsed until the human player made a decision. </p> </li> <li> <p>unclipped_reward: Integer. Immediate reward returned by the game engine.</p> </li> <li> <p>action: Integer. See action_enums.txt for the mapping. This is consistent with the Arcade Learning Environment setup.</p> </li> <li> <p>gaze_positions: Null/A list of integers: x0,y0,x1,y1,...,xn,yn. Gaze positions for the current frame. Could be null if no gaze. (0,0) is the top-left corner. x: horizontal axis. y: vertical.</p> </li> </ul> <p> </p> <p>3. action_enums.txt: contains integer to action mapping defined by the Arcade Learning Environment. </p> <p> </p> <p><strong>============================ Citation ============================</strong></p> <p>If you use the Atari-HEAD in your research, we ask that you please cite the following:</p> <p>@misc{zhang2019atarihead,</p> <p> title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},</p> <p> author={Ruohan Zhang and Calen Walshe and Zhuode Liu and Lin Guan and Karl S. Muller and Jake A. Whritner and Luxin Zhang and Mary M. Hayhoe and Dana H. Ballard},</p> <p> year={2019},</p> <p> eprint={1903.06754},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={cs.LG}</p> <p>}</p> <p>Zhang, Ruohan, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. "AGIL: Learning attention from human for visuomotor tasks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 663-679. 2018.</p> <p>@inproceedings{zhang2018agil,</p> <p> title={AGIL: Learning attention from human for visuomotor tasks},</p> <p> author={Zhang, Ruohan and Liu, Zhuode and Zhang, Luxin and Whritner, Jake A and Muller, Karl S and Hayhoe, Mary M and Ballard, Dana H},</p> <p> booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},</p> <p> pages={663--679},</p> <p> year={2018}</p> <p>}</p> <p><br> <br> </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.