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Simulating Intrastand Movement of Hemlock Woolly Adelgid at Harvard Forest 2009
The hemlock woolly adelgid (HWA), Adelges tsugae Annand (Hemiptera: Adelgidae), has spread rapidly across the eastern USA since its introduction from Japan 60 years ago, causing widespread mortality of both eastern hemlock [Tsuga canadensis (L.) Carriere] and Carolina hemlock [Tsuga caroliniana Engelm. (Pinaceae)]. Although HWA spread patterns have been repeatedly analyzed at regional scales, comparatively little is known about its dispersal potential within and between hemlock stands. As the small size and clonal nature of HWA make it nearly impossible to identify the source populations of dispersing individuals, we simulated intrastand HWA movement in the field by monitoring the movement of clumps of fluorescent powder that are slightly larger than HWA, but much easier to detect in the forest understory. Using three hemlock trees with three colors of fluorescent powder as source populations, we detected dispersal events at the farthest distances within our trapping array (400 m). However, more than 90% of dispersal events were less than 25 m. Dispersal patterns were similar from all three source trees and the distribution of dispersal distances in all cases could be described by lognormal probability density functions with mean dispersal distance of 12-14 m, suggesting that dispersal was relatively independent of location of source trees. In general, we documented tens of thousands of passive dispersal events in the forest understory despite the presence of a dense forest canopy. Thus, even under relatively light-wind conditions, particles of similar dimensions to HWA are capable of intra-stand movement, suggesting that a large population of HWA could rapidly infest other trees within several hundred meter radius, or beyond.
Hydrodynamic Model Output Used to Evaluate Chinook Salmon Movements and Distribution in the South Delta
This data release includes the output variables extracted from the UnTRIM Bay-Delta hydrodynamic model (hydrodynamic model) for use in evaluating the effects of hydrodynamics on the behavior of acoustically-tagged juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin Delta. Work was funded by State Water Contractors (SWC) and completed by Anchor QEA; FlowWest, LLC; and University of Washington under a SWC 2023 Science Plan grant (study name Evaluation of the Influence of State Water Project and Central Valley Project on Chinook Salmon Movements and Distribution in the South Delta), contracted by SWC. Not all the hydrodynamic model output variables in the output provided with this memorandum were used in the final fish models used to analyze Chinook Salmon responses. Model output for additional variables and locations were included for completeness and to make these output files more broadly useful to researchers interested in other locations or variables in the Sacramento-San Joaquin Delta. Hydrodynamic model simulations were conducted for 2010, 2011, 2012, 2013, 2014, 2015, 2016, and 2017, with hydrodynamic model output variables provided at mostly the same locations for each period simulated. The years 2011 through 2016 were simulated previously for a prior project and model output provided through the Environmental Data Initiative (edi.1124.1). Files for these years were recreated from the prior simulations for this project to add an output location. Additional locations were added to the 2010 and 2017 simulations for the 2010 and 2017 hydrophone arrays, and thus 2010 and 2017 include additional model output, relative to 2011 through 2016. The model simulation for each year spanned the full period of Chinook Salmon detections in the telemetry data collected during that year.
Movements of aquatic predators within the Shark River estuary (FCE LTER), Everglades National Park, South Florida, USA, June 2007 - ongoing
In South Florida, the allocation of freshwater resources is a constant source of debate. Stakeholders competing for freshwater include agriculture, rapidly growing urban populations, and the natural environment with its associated ecosystem services. Among these services, one of the most valuable is the provisioning of coastal recreational fisheries, which generates roughly $8 billion annually in angler expenditures in Florida alone. Yet, the interplay between freshwater allocation and the sustainability of these coastal fisheries remains poorly understood. One pathway of influence is through the availability of resources and food. Seasonal rainfall and freshwater management drive pulses of freshwater marsh prey into estuaries, creating short-lived but abundant foraging opportunities. Previous research has shown that these prey pulses occur primarily in the inland reaches of the estuary, providing resources for recreationally and ecologically important consumers such as the Common Snook (Centropomus undecimalis), Florida Largemouth Bass (Micropterus salmoides), Red Drum (Sciaenops ocellatus), Atlantic Tarpon (Megalops atlanticus), Bull Shark (Carcharhinus leucas), and American Alligator (Alligator mississippiensis). However, it is unclear how far these species move to exploit this subsidy, or whether such pulses increase reproductive output and long-term population stability. Further, sea level rise is changing how economically and ecologically important taxa use estuarine environments. To address these questions, we use acoustic telemetry to track the multi-year (2007–present) movements of key estuarine taxa, including Common Snook, Florida Largemouth Bass, American Alligator, and Bull Shark, within the Shark River Estuary of Everglades National Park. This multi-species approach expands our focus from freshwater and estuarine predators to include apex predators that link freshwater, estuarine, and marine ecosystems. From a science perspective, our research provides
Disentangling the percepts of illusory movement and sensory stimulation during tendon vibration in the EEG
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THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Interaction
<h1>The THÖR-MAGNI Dataset Tutorials</h1> <p>THÖR-MAGNI datasets is a novel dataset of accurate human and robot navigation and interaction in diverse indoor contexts, building on the previous <a href="https://ieeexplore.ieee.org/abstract/document/8954833/">THÖR dataset protocol</a>. We provide position and head orientation motion capture data, 3D LiDAR scans and gaze tracking. In total, THÖR-MAGNI captures <strong>3.5 hours of motion of 40 participants on 5 recording days</strong>.</p> <p>This data collection is designed around systematic variation of factors in the environment to allow building cue-conditioned models of human motion and verifying hypotheses on factor impact. To that end, THÖR-MAGNI encompasses 5 scenarios, in which some of them have different conditions (i.e., we vary some factor):</p> <ul> <li>Scenario 1 (plus conditions A and B): <ul> <li> Participants move in groups and individually;</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles and lane marking on the floor for <strong>condition B</strong>;</li> </ul> </li> </ul> <ul> <li> Scenario 2: <ul> <li> Participants move in groups, individually and transport objects with variable difficulty (i.e. bucket, boxes and a poster stand);</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 3 (plus conditions A and B): <ul> <li> Participants move in groups, individually and transporting objects with variable difficulty (i.e. bucket, boxes and a poster stand). We denote each role as: <em>Visitors-Alone, Visitors-Group 2, Visitors-Group 3, Carrier-Bucket, Carrier-Box, Carrier-Large Object;</em></li> <li> Teleoperated robot as moving agent: in <strong>condition A</strong>, the robot moves with differential drive; in <strong>condition </strong>B, the robot moves with omni-directional drive;</li> <li> Environment with 2 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 4 (plus conditions A and B): <ul> <li> All participants, denoted as <em>Visitors-Alone HRI</em> interacted with the teleoperated mobile robot;</li> <li> Robot interacted in two ways: in <strong>condition A</strong> (Verbal-Only), the Anthropomorphic Robot Mock Driver (ARMoD), a small humanoid NAO robot on top of the mobile platform, only used speech to communicate the next goal point to the participant; in <strong>condition B</strong> the ARMoD used speech, gestures and robotic gaze to convey the same message;</li> <li> Free space environment</li> </ul> </li> </ul> <ul> <li>Scenario 5: <ul> <li> Participants move alone (<em>Visitors-Alone</em>) and one of the participants, denoted as <em>Visitors-Alone HRI</em>, transport objects and interact with the robot;</li> <li> The ARMoD is remotely controlled by an experimenter and proactively offers help;</li> <li> Free space environment;</li> </ul> </li> </ul> <h2>Preliminary steps</h2> <p>Before proceeding, make sure to download the data from ZENODO</p> <h3>1. Directory Structure</h3> <p>├── CLiFF_Maps <- Directory for CLiFF Maps for all files</p> <p> ├── Files <- Directory for the csv files</p> <p> ├── Readme.md</p> <p>├── CSVs_Scenarios <- Directory for aligned data for all scenarios</p> <p> ├── Scenario_1 <- Directory for the csv files for Scenario 1</p> <p> ├── Scenario_2 <- Directory for the csv files for Scenario 2</p> <p> ├── Scenario_3 <- Directory for the csv files for Scenario 3</p> <p> ├── Scenario_4 <- Directory for the csv files for Scenario 4</p> <p> ├── Scenario_5 <- Directory for the csv files for Scenario 5</p> <p>├── docs</p> <p> ├── tutorials.md <- Tutorials document on how to use the data</p> <p>├── Lidar_sample</p> <p> ├── Files <- Directory for sample files</p> <p> ├── 170522_SC3B_1 <- Directory for the pcd files</p> <p> ├── 170522_SC3B_1.csv <- Synchronization file with QTM</p> <p> ├── manual_view_point.json <- json file with manual view point for visualization</p> <p> ├── requirements.txt <- script pip requirements</p> <p> ├── visualize_pcd.py <- script visualize the lidar data</p> <p> ├── Readme.md</p> <p>├── maps <- Directory for maps of the environment (PNG files) and offsets (json file)</p> <p> ├── offsets.json <- Offsets of the map with respect to the global coordinate frame origin</p> <p> ├── {date}_SC{sc_id}_map.png <- Maps for `date` in {1205, 1305, 1705, 1805} and `sc_id` in {1A, 1B, 2, 3}</p> <p> ├── 3009_map.png <- Map for the Scenarios 4A, 4B and 5</p> <p>├── MP4_Videos</p> <p> ├── Files <- Directory for the mp4 files</p> <p> ├── pupil_scene_camera_instrinsics.json <- json file with the intrinsics of pupil camera</p> <p>├── TSVs_RAWET <- Directory for the TSV files for the Raw Eyetracking data for all Scenarios</p> <p> ├── synch_info.csv <- Event markers necessary to align motion capture with eyetracking data</p> <p> ├── Files <- Directory with all the raw eyetracking TSV files</p> <p>├── goals_positions.csv <- File with the goals locations</p> <p> </p> <h3>2. Data Structure and Dataset Files</h3> <p>Withing each Scenario directory, each csv file contains:</p> <p><strong>2.1. Headers</strong></p> <p>The dataset metadata overview contains important information found in the CSV file headers. This reference is designed to help users understand and use the dataset effectively. The headers include details such as FILE_ID, which provides information on the date, scenario, condition, and run associated with each recording. The header of the document includes important quantities such as the number of frames recorded (N_FRAMES_QTM), the count of rigid bodies (N_BODIES), and the total number of markers (N_MARKERS).</p> <p>It also provides information about the order of the contiguous rotation matrix (CONTIGUOUS_ROTATION_MATRIX), modalities measured with units, and specified measurement units. The text presents details on the eyetracking devices used in each recording, including their infrared sensor and scene camera frequencies, as well as an indication of the presence of eyetracking data.</p> <p>The header provides specific information about rigid bodies, including their names (BODY_NAMES), role labels (BODY_ROLES), and the number of markers associated with each rigid body (BODY_NR_MARKERS). Finally, the table lists all marker names used in the file.</p> <p>This metadata provides researchers and practitioners with essential guidance on recording information, data quantities, and specifics about rigid bodies and markers. It is a valuable resource for understanding and effectively using the dataset in the CSV files.</p> <p><strong>2.2. Trajectory Data</strong></p> <p>The remaining portion of the CSV file integrates merged data from the motion capture system and eye tracking devices, organized based on participants' helmet rigid bodies. Columns within the dataset include XYZ coordinates of all markers, spatial centroid coordinates, 6DOF orientation of the object's local coordinate frame, and <em>if available</em> eye tracking data, encompassing 2D/3D gaze coordinates, scene recording frame numbers, eye movement types, and IMU data.</p> <p>Missing data is denoted by "N/A" or an empty cell. Temporal indexing is facilitated by the "Time" or "Frame" column, indicating timestamps or frame numbers. The motion capture system records at 100Hz, Tobii Glasses at 50Hz (Raw); 25 Hz (Camera), and Pupil Glasses at 100Hz (Raw); 30 Hz (Camera). The dataset is structured around motion capture recordings, and for each rigid body, such as "Helmet_1," details per frame include XYZ coordinates of markers, centroid coordinates, and a 9-element rotational matrix describing helmet orientation.</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td>Helmet_1 - 1 X</td> <td>X-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Y</td> <td>Y-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Z</td> <td>Z-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - [...]</td> <td><em>Same for Marker 2 and 3 of Helmet_1</em></td> </tr> <tr> <td>Helmet_1 Centroid_X</td> <td>X-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Y</td> <td>Y-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Z</td> <td>Z-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 R0</td> <td>1st Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> <tr> <td>Helmet_1 R[..]</td> <td>Same for R1- R7</td> </tr> <tr> <td>Helmet_1 R8</td> <td>9th Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> </tbody> </table> <p> </p> <p><strong>2.3. Eyetracking Data</strong></p> <p>The eye tracking data in the dataset includes 16 participants, providing a comprehensive dataset of over 500 minutes of recorded data across the different activities and scenarios with three different eyetracking devices. Devices are denoted with a special "Tracker_ID" in the dataset, i.e.:</p> <table> <tbody> <tr> <td><strong>Tracker ID</strong></td> <td><strong>Eyetracking Device</strong></td> </tr> <tr> <td>TB2</td> <td>Tobii 2 Glasses</td> </tr> <tr> <td>TB3</td> <td>Tobii 3 Glasses</td> </tr> <tr> <td>PPL</td> <td>Pupil Insivisible Glasses</td> </tr> </tbody> </table> <p>Gaze points are classified into fixations and saccades using the Tobii I-VT Attention filter, which is specifically optimized for dynamic scenarios with a velocity threshold of 100°. Eyetracking devices were systematically repeated after each 4-minute recording to account for natural variations in participants' eye shapes and to improve the gaze estimation algorithms. In addition, gaze estimation adjustments for the pupil invisible glasses were made after each 4-minute recording to mitigate potential drifts. It's worth noting that the scene cameras of the eye tracking glasses had different fields of view. The scene camera of the Pupil Invisible Glasses had a 1088x1080 image with both horizontal (HFOV) and vertical (VFOV) opening angles of 80°, while the Tobii Glasses provided a 1920x1080 image with different opening angles for Tobii Glasses 3 (HFOV: 95°, VFOV: 63°) and Tobii Glasses 2 (HFOV: 82°, VFOV: 52°).</p> <p><strong>NOTE AS OF 2024:</strong> <strong>Videos are NOW part</strong> of the dataset</p> <p>For one participant, wearing the Tobii Glasses 3 and Helmet_6, the data would be denoted as:</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td><em>Helmet_6 - [...]</em></td> <td><em>*X,Y,Z Coordinates for 5 markers*</em></td> </tr> <tr> <td><em>Helmet_6 [...]</em></td> <td><em>X,Y,Z Coordinates for 1 Centroid* </em></td> </tr> <tr> <td><em>Helmet_6 R[...]</em></td> <td><em>9 Elements of the CONTIGUOUS_ROTATION_MATRIX</em></td> </tr> <tr> <td> <p>Helmet_6 TB3_Accelerometer_[...]</p> </td> <td>Accelerometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Gyroscope_[...]</td> <td>Gyroscope data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Magnetometer_[...]</td> <td>Magnetometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_G2D_[...]</td> <td>2D Eye tracking data (X,Y)</td> </tr> <tr> <td>Helmet_6 TB3_G3D_[...]</td> <td>3D Cyclopic Eye gaze Vector (X,Y,Z)</td> </tr> <tr> <td>Helmet_6 TB3_Movement</td> <td>Eye movement type (N/A, Fixation or Saccade)</td> </tr> <tr> <td>Helmet_6 TB3_SceneFNr</td> <td>Frame number of the scene camera recording </td> </tr> </tbody> </table> <h2>How to use and tools</h2> <p><a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">magni-dash</a></p> <p><a href="https://magni-dash.streamlit.app">This</a> is a dashboard to quickly visualize our data: trajectories, speeds, eye-tracking data and LiDAR visualization (for Scenario 3). If you cannot use the dashboard from the streamlit cloud service, just run it locally by following the <a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">README File</a>.</p> <p><a href="https://github.com/tmralmeida/thor-magni-tools">thor-magni-tools</a></p> <p>To install and use the package, follow the instructions on the <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/README.md">README file</a> . This package comprises:</p> <ul> <li>3D trajectory restoration: agents in the scene wore an helmet. The helmet is equipped with markers, which are tracked by the Mocap system. 3D trajectory restoration stands for <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/thor_magni_tools/preprocessing/cfg.yaml#L3">two different ways</a> of aggregating the trackings of the various markers in each helmet: (1) <em>3D-restoration</em> and (2) <em>3D-best marker</em>. The former applies an average over the locations of all visible markers while the latter uses the marker with highest tracking duration.</li> <li>3D pre-processing of restored trajectories: interpolation, downsampling and smoothing. To run the 3D pre-processing, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#preprocessing">this</a>.</li> <li>trajectory analysis: trajectory-related metrics like tracking duration (in seconds), number of 8s <em>tracklets</em>, motion speed, path efficiency score, and minimal distance between people. To run the trajectory analysis, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#analysis">this</a>.</li> </ul>
Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease
<p>Movement data was collected through smartwatches to monitor motor competence in subjects with Parkinson's Disease (PD). The data set collected for the Monipar study consists of triaxial acceleration data from 21 subjects with PD and 7 healthy control subjects when performing a set of physical exercises while wearing an off-the-shelf smartwatch. Each participant performed the complete set of eight exercises once a week, commonly on the same day and at a similar time. Three Matlab files are provided that contain the raw data of the experimental subgroups: (1) Supervised, (2) Remote, and (3) Healthy control. Additionally, two Matlab files are provided containing the Tremor Labels for selected subjects in the experimental subgroups: (1) Supervised and (2) Remote.</p><p>While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:</p><p>Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.</p><p>Supervised group: This subgroup comprised PD patients who underwent the experimental protocol under circumstances similar to the remote group. Additionally, clinical scoring (MDS-UPDRS) is reported for this group in the file "MONIPAR SUBJECTS DATA.xlsx"</p><p>Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.</p><p>Data was collected using a sample rate of 50Hz and expressed in m/s^2.</p><p>Check the "Monipar_README.txt" file for details about this dataset. Further details are contained in the following reference -- if you use this dataset, please cite:</p><p>Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "<strong>Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones</strong>". <i>Frontiers in Neurology</i>, <i>14</i>, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a></p><p>References:</p><p>Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879</p><p>Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.</p><p><strong>Funding:</strong></p><p>This research was funded by the following projects:</p><p>(1) "Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP).</p><p>(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.</p>
Audio and Water Movement Data for Oyster Reef and Mudflat Sites on the Coast of Virginia, 2018
Paired marine audio soundscape recordings with concurrent acoustic Doppler velocimeter (ADV) turbulence measurements at three intertidal sites: a natural oyster reef, a restored oyster reef, and a bare mudflat. The objective was to establish the link between oyster reef soundscapes and hydrodynamics, specifically the role that turbulence plays in generating near field pressure waves that may be used as physical cues by oyster larvae when initiating settlement behaviors. Data were collected at three different locations, with water turbulance measurements at rates up to 25Hz concurrent with audio recording.
Data on anatomy, movement, and foraging behaviour of three cattle breeds of different productivity
<p>Given are</p> <ul> <li>the breed of the cattle (AH: Angus×Holstein, OB: Original Braunvieh, HC: Highland cattle),</li> <li>the age of the cows in months,</li> <li>the body weight at the beginning (Weight_1) and the end (Weight_2) of the experiment in kg,</li> <li>the summarised base of all eight claws of each cow in cm<sup>2</sup>,</li> <li>the average number of steps per hour as recorded by the pedometer,</li> <li>the average speed in m h<sup>-1</sup>,</li> <li>the ratio of the time spent lying as recorded by the pedometer,</li> <li>the evenness of space use calculated as Camargo’s index based on GPS positions,</li> <li>the evenness of forage selection calculated as Pielou’s evenness,</li> <li>the average forage quality indicator value (Briemle, Nitsche, and Nitsche 2002) of the selected diet,</li> <li>the ratio of broad leaved grasses, legumes, thistles and shrubs within the diet of each cow.</li> </ul> <p>All measurements conducted on the pastures are presented as averaged over all pastures (xxx_mean) and separatly for the three pastures (xxx_1, xxx_2, xxx_3).</p>
Data set and code supporting Marshall et al. 2020. No room to roam: King Cobras reduce movement in agriculture.
<p>Data and code used in the publication:</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. <em>Mov Ecol</em> <strong>8, </strong>33 (2020). https://doi.org/10.1186/s40462-020-00219-5</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. bioRxiv 2020.03.24.006676; doi: https://doi.org/10.1101/2020.03.24.006676</p> <p>Including: telemetry data, habitat shapefile and derived rasters, ISSF and JAGS model specification and results, code to reproduce analysis and generate figures. </p>
Outer Port of Punta Langosteira (Spain) ship movement dataset: 2021 - 2022
<p>This dataset contains the movements of 21 ships recorded in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2021 until 2022.</p>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Begomovirus DNA-B Movement Protein and Nuclear Shuttle Protein Ref-Seq Datasets
<p>Multiple Sequence Alignment of proteins encoded on begomovirus DNA-B (movement protein and nuclear shuttle protein; n=131). One isolate per species based on ICTV reference list.</p> <p> </p>
Data and analysis for Association of meeting 24-hour movement guidelines with low back pain among adults
<p>Introduction</p> <p>This data and code forms the analytical process of a study examining associations between meeting different combinations of 24-h movement guidelines (that integrates a recommendations on physical activity, sedentary behaviour, and sleep) with prevalence, frequency and intensity of low back pain in a sample of adults aged 18 years and over. </p> <p>Notes: </p> <p>* the raw data is provided alongside this upload, but the processing is not addressed here. <br> * the authors of this document are a subset of the authors of the related paper.<br> * this document and the related data files were uploaded at the time of submission for review. An update providing the doi of the related paper will be provided when it is available.</p>
Kinematic and Electromyographic Recordings during Dynamic, Repetitive, Low-Force Movements
<p>Experimental recordings of kinematic (XSens Awinda, Full-Body) and electromyographic (Delsys Trigno, 8 Right Arm Muscles) data during 4 repetive upper limb exercises with a 1.5Kg dumbell: A) elbow counter-gravity flexion and gravity-assisted extension, with torso tilted forwards; B) shoulder counter-gravity abduction and gravity-assisted adduction; C) shoulder counter-gravity flexion and gravity-assisted extensio; D) composite sequence of elbow/shoulder flexion/extension motions, executed until self-reported fatigue.</p> <p>A total of 17 healthy volunteers (11 Male; 6 Female; 23.82 ± 2.79 years old, 69.06 ± 14.75 Kg) were recruited. Each participant willingly agreed to participate in the study and gave their signed, informed consent, following the standard set by the declaration of Helsinki and the Oviedo Conventions.</p> <p>Additionally, self-reported fatigue after each exercise, according to Borg's Perceived Exertion Scale (Borg, 1998), is provided for all subjects.</p> <p>For a detailed description of the experimental protocol and instrumentation, refer to associated research paper (submission under review).</p>
Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality Dataset
<p>This dataset accompanies the paper “Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality” published in the Computer and Graphics journal from Elsevier. It contains 3 datasets related to the experiments described in the paper. All datasets are in “.csv” format and can be easily loaded by statistical analysis tools (e.g. a dataset can be loaded in r using the command read.csv(“filename.csv”)). It also contains the C# Unity implementation of the distortion function presented in the paper.</p> <p>Paper reference:</p> <p>Galvan Debarba H, Boulic R, Salomon R, Blanke O, Herbelin B. Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality. Computers & Graphics. 2018; ISSN 0097-8493. Elsevier.</p> <p>DOI: doi.org/10.1016/j.cag.2018.09.001</p>
Data for: Experimental data about the evacuation of preschool children from nursery schools, Part II: Movement characteristics and behaviour
<p>These datasets contain supplementary material for the article " Experimental data about the evacuation of preschool children from nursery schools, Part II: Movement characteristics and behaviour " accepted to Fire Safety Journal on April 16, 2023 (DOI <a href="https://doi.org/10.1016/j.firesaf.2023.103797">10.1016/j.firesaf.2023.103797</a>). The article presents experimental data sets on the evacuation movement and behaviour of preschool children observed during 15 evacuation drills in 10 nursery schools in the Czech Republic involving 970 children (3-7 years of age) and 87 staff members. </p> <p>In the presented spreadsheets, raw experimental data on speed-density and flow-density relationships are provided separately for corridors, straight staircases (flights, landings, and entire staircase), and doorways. The movement travel speed ('Speed') is expressed in [m·s<sup>-1</sup>], specific flow ('Flow') in [pers·s<sup>-1</sup>·m<sup>-1</sup>], density variable is expressed in the units of [pers·m<sup>−2</sup>] ('Density1') and [m<sup>−2</sup>·m<sup>−2</sup>] ('Density2'). Observations made for the different age groups of children are distinguished by letters: 'J' – Junior, 'S' – Senior, 'S+' - Senior+, 'M' – Mixed. In speed-density data sets, observations for walking children are denoted as 'W', for running children as 'R' (e.g., 'JW' – Junior walking). Speed-density data points that were calculated by excluding waiting times of children (only in the spreadsheets for corridors and landings of straight staircases) are marked 'M' after age group denotation (e.g., 'SWM-Speed' – modified speed data points for Senior walking children).</p>
Analyzing fish leaping and movement potential at a migratory barriers: Michigan 2022-2025
The enclosed Matlab scripts and supporting files are used for fish passage assessments at small migratory barriers with complex geometry. Building upon a 2-D ballistic trajectory mathematical model, the model incorporates field-derived behavioral parameters and simulated flow data to evaluate the likelihood of successful leaping attempts. The model integrates stochastic variation in key fish characteristics—such as body length, launch speed, and leap origin—to simulate a range of realistic leaping scenarios. These are coupled with three-dimensional, CFD-derived inputs for local water velocity and depth, enabling a spatially detailed assessment of hydraulic conditions that are most conducive to successful passage. This integrated approach provides a more comprehensive and biologically informed evaluation of passage potential at migratory barriers. To demonstrate its utility, the model is applied in a case study examining the influence of complex barrier geometry on fish passage outcomes, highlighting its potential to inform engineering design decisions that either facilitate or limit fish movement based on management objectives. The enclosed files are associated with steelhead passage at a arc-labyrinth and low-flow weir at the FishPass project, located on the Boardman/Ottaway River, MI, USA.
CBM01 Plains bison movement patterns in an experimental heterogeneous landscape at Konza Prairie
This GPS-collar data set was used to evaluate the factors that influence where bison choose to graze and how grazing and space use patterns affect ecosystem function and structure. Our objectives were to quantify space use and movement patterns of adult female Plains bison in the context of selection for specific prescribed burn frequencies and topographical features in the bison-grazed watersheds at Konza Prairie. We hypothesized bison would track post-prescribed burn forage productivity and we predicted watersheds burned for the first time in several years would be used to a greater extent than watersheds burned more frequently.
Datasets with and without deliberate head movements for detection and imputation of dropout in diffusion MRI
Open the record for dataset details and reuse information.
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</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.