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198 results for “poses”
RNAPosers: Machine Learning Classifiers For RNA-Ligand Poses [Data Set]
<ul> <li>This dataset contains the decoys poses used to train and test RNAPosers, a set of RNA-ligand pose classifiers.</li> <li>The folder of each RNA-ligand complex (identified using its PDB ID) contains: <ul> <li>Ligand SMILES: lig.smi</li> <li>Ligand coordinate: lig.sd</li> <li>Receptor coordinate: receptor.mol2</li> <li>Pose coordinates: poses.sd</li> <li>Pose similarity data: rmsd.txt</li> </ul> </li> </ul>
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
<p>This data entry contains <strong>annotated mouse data from the <a href="https://www.nature.com/articles/s41593-018-0209-y">DeepLabCut Nature Neuroscience paper</a></strong>.</p> <p>This data entry contains a public release of annotated mouse data from the DeepLabCut paper. The trail-tracking behavior is part of an investigation into odor guided navigation, where one or multiple wildtype (C57BL/6J) mice are running on a paper spool and following odor trails. These experiments were carried out by Alexander Mathis & Mackenzie Mathis in the Murthy lab at Harvard University. </p> <p>Data was recorded by two different cameras (640×480 pixels with Point Grey Firefly (FMVU-03MTM-CS), and at approximately 1,700×1,200 pixels with Grasshopper 3 4.1MP Mono USB3 Vision (CMOSIS CMV4000-3E12)) at 30 Hz. The latter images were cropped around mice to generate images that are approximately 800×800. </p> <p>Here we share 1066, frames from multiple experimental sessions observing 7 different mice. Pranav Mamidanna labeled the snout, the tip of the left and right ear as well as the base of the tail in the example images. The data is organized in <a href="https://www.nature.com/articles/s41596-019-0176-0">DeepLabCut 2.0 project structure</a> with images and annotations in the labeled-data folder. The names are pseudocodes indicating mouse id and session id, e.g. m4s1 = mouse 4 session 1.</p> <p>Code for loading, visualizing & training deep neural networks available at <a href="http://https://github.com/DeepLabCut/DeepLabCut"> https://github.com/DeepLabCut/DeepLabCut</a>.</p>
Extending the executability of assembly task poses by robot through end-effectors
<p><strong>Extending the executability of assembly task poses by robots through end-effectors</strong></p> <p><br><em>Aline Kluge-Wilkes, Presley Demuner Reverdito</em></p> <p>The following data set was created during the validation of a proposed method to evaluate assembly station formations considering the executability of assembly tasks, including the effects of equipped end-effectors on robots.</p> <p>The underlying paper can be found at: https://doi.org/10.1007/978-3-031-34821-1_58 </p> <p>The underlying source code can be found at: https://git-ce.rwth-aachen.de/wzl-mq-public/iop/ws-b2.iv_formation-planning-of-mobile-robots/end-effector-dependent-executability</p> <p>In dependence on the geometries and degrees of freedom of the equipped end-effectors on the robot, a single task pose is transferred into an area of feasible task poses. Determining the executability of the task, the resulting representation of the feasible task poses is overlapped with the robot's workspace. If an overlap occurs, it can be assumed that there is a feasible robot configuration to execute the allocated assembly task. The proposed method is exemplified on a UR10 equipped with a screwdriver, and distributed task poses. The proposed method quantifies the end-effector's effect on the executability of assembly tasks and provides a means of determining the feasible base placement of robots in changeable assembly stations. Therefore, the method lays the foundation for automated formation planning in assembly stations.</p> <p><strong>Design of Experiments:</strong></p> <p>The published data here results from a conducted series of experiments structured as a full-factorial design of experiments. The following variables and expressions / data points of those variables were chosen:</p> <p>Tasks ("GoalPose") poses as [x, y, z, x-quaternion, y-quaternion, z-quaternion, w-quaternion]:</p> <ul> <li> Task 1 = (0.416m ,-0.399m, 0.764m, 0.071, 0.703, 0.134, 0.694)</li> <li> Task 2 = (0.438m, -0.647m, 0.816m, 0.005, 0.707, 0.068, 0.704)</li> <li> Task 3 = (0.448m, -0.755m, 0.945m, -0.243, 0.664 ,-0.183, 0.683)</li> </ul> <p>Dimension of the tools ("DimensionOfTool") as [x, y, z]: </p> <ul> <li> (0.1m, 0.2m, 0m)</li> <li> (0.2m, 0.1m, 0m)</li> </ul> <p>Robot model ("RobotModel"): </p> <ul> <li> UR10 (https://www.universal-robots.com/de/produkte/ur10-roboter/)</li> <li> UR5 (https://www.universal-robots.com/products/ur5-robot/)</li> </ul> <p>Resolution of reachability map ("ReachMap"): </p> <ul> <li> 0.05m</li> <li> 0.08m</li> <li> 0.1m</li> </ul> <p>IK solver ("IKSolver"): </p> <ul> <li>TRAC-IK <ul> <li>documented in: http://docs.ros.org/en/kinetic/api/moveit_tutorials/html/doc/trac_ik/trac_ik_tutorial.html </li> <li>source: https://bitbucket.org/traclabs/trac_ik/src/master/</li> </ul> </li> <li>KDL solver <ul> <li>https://docs.orocos.org/kdl/overview.html</li> </ul> </li> </ul> <p>Based on these definitions, a fully factorial design was implemented, resulting in 72 required tests (2³ × 3²). These tests were randomly organized into a single experimental block.</p> <p><strong>Test execution:</strong></p> <p>The function "[calcForAllTasks](https://git-ce.rwth-aachen.de/wzl-mq-public/iop/ws-b2.iv_formation-planning-of-mobile-robots/end-effector-dependent-executability/-/blob/main/code/main.py?ref_type=heads)" on the file main.py was created to generate 72 files, each containing 10 poses of the circle of possible poses for the robot with a calculated reachability map. These poses were manually tested using MoveIt! (https://moveit.ros.org/) and using two different IK solvers, KDL and TRAC-IK.</p> <p>There were now four packages: ur5_kdl, ur5_trac_ik, ur10_kdl, and ur10_trac_ik. Each package had its own group_name, which was the same as the package name. For each test, it was necessary to run the MoveIt! file of the robot with the correspondent IK solver, and the [move_group_python_interface.py](https://git-ce.rwth-aachen.de/wzl-mq-public/iop/ws-b2.iv_formation-planning-of-mobile-robots/end-effector-dependent-executability/-/blob/main/code/move_group_python_interface.py?ref_type=heads) with the desired pose. On the Python code, the goal pose was set, and using the MoveGroupPythonIntefaceTutorial class, Moveit! attempt to move the robot to the desired pose. If the pose is reachable, the reachability index was set to 1. Otherwise, it was set to 0, and the terminal output would show "ABORTED: No motion plan found. No execution attempted."</p> <p>After simulating all poses, the reachability average of each test was calculated, along with the sum of reachable results. The reachability index ranged from 0 to 100, and the reachable column ranged from 0 to 10. This result can be found in the file DoE-tests-and-results.xlsx.</p> <p> </p> <p>The file "DoE-tests-and-results.xlsx" provides an overview of all experiments in its first table. The creation order ("StdOrder") and the order in which the experiments were conducted are given ("RunOrder"). The following columns in the first table indicate the expressions of the variables as explained above (GoalPose, DimensionOfTool, RobotMode, ReachMap, IKSolver). Next, the results of the experiments are given: the average reachability index per tested goal pose and the indication of how many of the ten discrete robot flange poses per goal pose are reachable by the robot (Reachability Index, Reachable). </p> <p>The following 72 tables each provide the ten robot flange poses per goal pose per experiment as [x, y, z, x-quaternion, y-quaternion, z-quaternion, w-quaternion]. </p> <p>The 72 .csv files contain the same information as the 72 tables in the Excel file but contain the information as it was transferred during execution. </p> <p> </p> <p> </p> <p>After all simulations, it is possible to conclude that the reachability depends on the position of the task and the model of the robot. For example, UR5 could not reach task 3, while the UR10 had a high value of reachability for it due to the fact that the UR10 has a bigger workspace.</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Acknowledgement:</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612.<br> </p>
Spacecraft Pose Estimation Dataset (SPEED)
<p>The SPEED dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>"Pose Estimation challenge" </strong>in collaboration<strong> with Stanford Universitiy's Space Rendezvous Lab (SLAB)</strong>. It features images and poses of the Tango spacecraft (PRISMA mission), 12000 of them generated by SLAB's Optical Simulator using a high fidelity texture model and 300 images from the TRON facility, using a physical mock-up model of Tango.</p> <p>The goal of the competition was estimate the relative pose (distance and orientation) from pixel images only.</p> <ul> <li>Detailed information about the original competition can be found at <a href="https://kelvins.esa.int/satellite-pose-estimation-challenge/">https://kelvins.esa.int/satellite-pose-estimation-challenge/</a></li> <li>A follow-up competition with a larger and improved dataset <strong>(SPEED+)</strong> is available on Zenodo as well: <a href="https://zenodo.org/record/5588480">https://zenodo.org/record/5588480</a></li> </ul> <p>A publication about the results of the pose estimation challenge has been published as</p> <ul> <li>Kisantal, Mate, et al. "Satellite pose estimation challenge: Dataset, competition design, and results." <em>IEEE Transactions on Aerospace and Electronic Systems</em> 56.5 (2020): 4083-4098.</li> </ul>
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>
MC-hands-1M: A glove-wearing hand dataset for pose estimation
<p>We introduce MC-hands-1M, a synthetic glove-wearing hand dataset for pose estimation. In the zip folder, there exist two subfolders: one containing roughly 750K images (Big set) and another with 250K images (Small set) along with the 2D camera plane and 3D world ground truth data of the corresponding poses. Each set is organized in folders named as Rendered View X, representing a specific camera in the 3D space with a fixed rotation and location. In each of those folders, there exist a json file containing corresponding data for the camera (location, rotation, intrinsics' matrix and images' relative paths) along with the aforementioned ground truth per image (pose). For each of those views, there exist other subfolders named as Scene 's Collection 's Objects' States' Combination Y. Each of those folders contains images of the different poses from the set camera view, given a different combination of background, lighting, glove- and cloth-like materials, and hand's a priori scaling state.</p><p>!!!!! IMPORTANT NOTE !!!!!</p><p>After some testing, an error in the ground truth of the small set of images was found. Please use only the big set found in the zip. For further instructions and questions, please contact us at prod@iti.gr.</p>
Operator and Robot Pose Data
<p>This is a dataset of a .bag file format.</p> <p>The data included in this dataset are collected during a 4 hour shift of an operator. The operator is working alongside a collaborative cobot (COMAU AURA). During his shift, the operator is performing the assembly of a solar collector. The collector is attached on the robotic gripper and is manipulated by the cobot, while the operator proceeds with the assembly. For this certain bag file, the ROS message definitions are included:</p> <p>---</p> <p>ROS Message: geometry_msgs/TransformStamped</p> <p>This expresses a transform from coordinate frame header.frame_id to the coordinate frame child_frame_id</p> <p><strong>Header </strong>header</p> <p><strong>string </strong>child_frame_id # the frame id of the child frame</p> <p><strong>Transform </strong>transform</p> <p>---</p> <p>ROS Message: std_msgs/Header</p> <p>Standard metadata for higher-level stamped data types. This is generally used to communicate timestamped data in a particular coordinate frame.</p> <p>sequence ID: consecutively increasing ID</p> <p><strong>uint32 </strong>seq</p> <p>Two-integer timestamp that is expressed as:</p> <p>stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')</p> <p>stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')</p> <p>time-handling sugar is provided by the client library</p> <p><strong>time </strong>stamp</p> <p>Frame this data is associated with</p> <p><strong>string </strong>frame_id</p> <p>---</p> <p>ROS Message: geometry_msgs/Transform</p> <p>This represents the transform between two coordinate frames in free space.</p> <p><strong>Vector3 </strong>translation</p> <p><strong>Quaternion </strong>rotation</p> <p>---</p> <p>ROS Message: geometry_msgs/Vector3</p> <p>This represents a vector in free space. It is only meant to represent a direction. Therefore, it does not make sense to apply a translation to it (e.g., when applying a generic rigid transformation to a Vector3, tf2 will only apply the rotation).</p> <p><strong>float64 </strong>x</p> <p><strong>float64 </strong>y</p> <p><strong>float64 </strong>z</p> <p>---</p> <p>ROS Message: geometry_msgs/Quaternion</p> <p>This represents an orientation in free space in quaternion form.</p> <p><strong>float64 </strong>x</p> <p><strong>float64 </strong>y</p> <p><strong>float64 </strong>z</p> <p><strong>float64 </strong>w</p>
OAK-D Synthethic Pose Dataset
<p>"OAK-D Synthethic Pose Dataset" is a synthetic image dataset of humans, annotated with 2d and 3d keypoints for a OAK-D like camera configuration.</p> <p>The dataset is part of the "3D Stereo Body Pose Estimation" project, originated in the context of the activities of the postgraduate course IA904 - Model Project in Visual Computing, offered in the first semester of 2024, at Unicamp, under the supervision of Prof. Dr. Leticia Rittner and Prof. Paula D. Paro Costa, both from the Department of Computer and Automation Engineering (DCA) of the Faculty of Electrical and Computer Engineering (FEEC).</p> <p>The <a href="https://github.com/Disciplinas-FEEC/IA904-2024S1/tree/main/projetos/3DStereoBodyPoseEstimation">project page</a> have a full description of the project (portuguese).</p>
3D Stereo Body Pose Estimation - Evaluation Plots
<p>Evaluation plots for the "3D Stereo Body Pose Estimation" project, aimed at estimating 3d keypoints from humans captured using a OAK-D camera.</p> <p>The project is originated in the context of the activities of the postgraduate course IA904 - Model Project in Visual Computing, offered in the first semester of 2024, at Unicamp, under the supervision of Prof. Dr. Leticia Rittner and Prof. Paula D. Paro Costa, both from the Department of Computer and Automation Engineering (DCA) of the Faculty of Electrical and Computer Engineering (FEEC).</p> <p>The <a href="https://github.com/Disciplinas-FEEC/IA904-2024S1/tree/main/projetos/3DStereoBodyPoseEstimation">project page</a> have a full description of the project (portuguese).</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>
Molecular dynamics simulations of the interaction of the quadruple mutant human CYP2J2 (R111A + R117A + R382A + R446A) with arachidonic acid (POSES 4-6)
<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>
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.