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479 results for “human interaction”
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>
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>
Anthropomorphic Mechanisms for User Acceptance in Human-Robot Interaction - PRISMA pass data
<p>This is the data produced in the course of selecting relevant literature for the <em>"User Acceptance in Human-Robot Interaction"</em> literature review article.</p> <p><strong>Contents:</strong></p> <ul> <li>Initial pass records: <em>prisma0_wos.xlsx + prisma0_scopus.xlsx</em></li> <li>Initial pass eligibility assessment:<em><strong> </strong>prisma0_eval.xlsx</em></li> <li>Second pass records, filtering and coarse assessment:<em><strong> </strong>prisma1.xlsx</em></li> <li>Third pass records, filtering and coarse assessment:<em><strong> </strong>prisma2.xlsx</em></li> <li>Fine eligibility assessment of 2nd and 3rd pass: <em>prisma_avalanche_1_and_2_report_update_04_26.pdf</em></li> </ul> <p> </p>
Collection of global datasets for the study of floods, droughts and their interactions with human societies
<p>This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to <a href="https://doi.org/10.1002/wat2.1424">Lindersson et al. (2020)</a> for further information about review methodology.</p> <p>The collection is a descriptive list, holding the following information for each dataset: </p> <ul> <li>Category<em> - as structured in Lindersson et al. (2020).</em></li> <li>Sub-category<em>- as structured in Lindersson et al. (2020).</em></li> <li>Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em></li> <li>Title <em>- full title of dataset.</em></li> <li>Product(s)<em> - type of product(s) offered by the dataset.</em></li> <li>Period<em> - time period covered by the dataset, not defined for all datasets.</em></li> <li>Temporal resolution<em> - not defined for static datasets.</em></li> <li>Angular spatial resolution<em> - only defined for gridded datasets.</em></li> <li>Metric spatial resolution <em>- only defined for gridded datasets.</em></li> <li>Map scale</li> <li>Extent<em> - geographic coverage of dataset given in latitude limits.</em></li> <li>Description</li> <li>Creating institute(s)</li> <li>Data type<em> - raster, vector or tabular.</em></li> <li>File format</li> <li>Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em></li> <li>Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em></li> <li>Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em></li> <li>Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em></li> <li>Citation<em> - dataset reference or credit.</em></li> <li>Documentation <em>- dataset documentation.</em></li> <li>Web address<em> - dataset access link.</em></li> </ul> <p>NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.</p>
Human Pleckstrin Homology domain Interacting Protein (PHIP); A Target Enabling Package
<p>SGC Oxford has expressed, purified and crystallized the second bromodomain of PHIP as part of the probe programme. Fragment screening and X-ray crystallography identified binders, some of which optimised to uM affinity. However, molecules with probe properties were not obtained. Consequently it has been decided to put the information generated into the public domain.</p>
HRI30: An Action Recognition Dataset for Industrial Human-Robot Interaction
<p>A thorough analysis of the existing human action recognition datasets demonstrates that only a few HRI datasets are available that target real-world applications, all of which are adapted to home settings. Therefore, given the shortage of datasets in industrial tasks, we aim to provide the community with a dataset created in a laboratory setting that includes actions commonly performed within manufacturing and service industries. In addition, the proposed dataset meets the requirements of deep learning algorithms for the development of intelligent learning models for action recognition and imitation in HRI applications.</p>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
Human-mouse syntenic Long Range Interactions in Neural Stem Cells
<p>This repository contains the list of human DNA regions corresponding to long-range interactions in RNA polII-mediated long-range interactions in mouse. They are named human-mouse syntenic Long Range Interactions (hmsLRI) and were obtained via synteny from mouse neonatal forebrain stem cells. They were also annotated for their overlap with DNA sequence variants (SNPs; CNVs) associated with human neurodevelopmental disease (NDD). The lists of genes identified via inferred that are potentially involved in NDD, eye-development, and traits (schizophrenia, bipolar disorder, intelligence).</p> <p>This is a resource for exploring the potential of the non-coding regions of DNA, the largest part of the genome (~98%), in NDD. Indeed, despite the numerous NDD-causative genes identified, only 42% of patients with severe developmental disorders carry pathogenic <em>de novo</em> mutations within coding sequences. More than a half of patient could be diagnosed and treated with further research and technologies, one option is studying the non-coding genome and how alterations affect genes. </p> <p>Tracks for visualization onto UCSC Genome Browser and WashU, are also provided.<br> See README for more details. </p> <p>The peer-reviewed publication for this dataset has now been published in International Journal of Molecular Science, available OA at: <a href="https://www.mdpi.com/1422-0067/23/14/7964">https://www.mdpi.com/1422-0067/23/14/7964</a>. Please cite this when using the dataset.</p>
Hand gesture dataset based on sEMG data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of five different/independent hand gestures are provided. The data were generated in a sEMG system with two bracelets (eight sEMG sensors and six sEMG sensors) worn in the right forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetForSegmentation.mat" was used to train a classifier whose purpose is the execution of Segmentation process. On the other hand, the file "datasetForRecognition.mat" was used to train a classifier whose purpose is the execution of gesture Recognition process.</p> <p> </p>
Arm gesture dataset based on IMU data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of ten different/independent hand gestures are provided (seven static gestures and three dynamic gestures). The data were generated in a IMU system with five sensors worn in the right forearm, right arm, chest, left arm and left forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetStaticGestures.mat" was used to train and test a classifier whose purpose is the recognition of static gestures. On the other hand, the file "datasetDynamicGestures.mat" was used to train and test a classifier whose purpose is the recognition of dynamic gestures. The latter file contains an extra class (gesture) which represents non-gestures.</p>
Molecular dynamics simulations of the interaction of the quadruple mutant human CYP2J2 (R111A + R117A + R382A + R446A) with arachidonic acid (POSES 1-3)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_quadmut_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of the quadruple R111A + R117A+R382A+R446A) mutant CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p>
Molecular dynamics simulations of the interaction of mutant human CYP2J2 (R117A) with arachidonic acid (POSES 5-6)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_mutR117A_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of the R117A mutant CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Molecular dynamics simulations of the interaction of wild type human CYP2J2 with DHA (POSES 1-4)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_wt_CYP2J2_DHA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of docosahexaenoic acid (DHA) in the active site of wild type CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 4 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Molecular dynamics simulations of the interaction of wild type human CYP2J2 with arachidonic acid (POSES 3 and 4)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_wt_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of wild type CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 4 times, hence there are 4 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Molecular dynamics simulations of the interaction of wild type human CYP2J2 with arachidonic acid (POSES 1 and 2)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_wt_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of wild type CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 4 times, hence there are 4 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Molecular dynamics simulations of the interaction of wild type human CYP2J2 with arachidonic acid (POSES 5 and 6)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_wt_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of wild type CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 4 times, hence there are 4 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Molecular dynamics simulations of the interaction of the double mutant human CYP2J2 (R117A and R111A) with arachidonic acid (POSES 1-3)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_mutR111A_R117A_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of the double R111A + R117A mutant CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p>
Molecular dynamics simulations of the interaction of mutant human CYP2J2 (R117A) with arachidonic acid (POSES 1-4)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_mutR117A_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of the R117A mutant CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Molecular dynamics simulations of the interaction of mutant human CYP2J2 (R111A) with arachidonic acid (POSES 4-6)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_mutR111A_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of the R111A mutant CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Molecular dynamics simulations of the interaction of wild type human CYP2J2 with EPA (POSES 1-4)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_wt_CYP2J2_EPA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of eicosapentaenoic acid (EPA) in the active site of wild type CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 4 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</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.