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Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability. Supplementary Video
<p>This video is a supplementary video material to the paper "Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability". It shows a comparision of five volumetric videos scenes, captured with three different sparse volumetric video applications. This video aims to visualize the difference in fidelity and artifacts that each system expresses.</p>
HEroBM: a deep equivariant graph neural network for high-fidelity backmapping from coarse-grained to all-atom structures
<p><span>Molecular simulations play a pivotal role in chemistry, biology, and material sciences, enabling the</span><br><span>study of complex dynamic properties within systems. Coarse-grained (CG) techniques have emerged</span><br><span>as indispensable tools in this domain, facilitating the sampling of large-scale systems and extending</span><br><span>simulation timescales by simplifying system representation. However, CG approaches involve a trade-</span><br><span>off: they sacrifice atomistic details that may be crucial for understanding the underlying processes.</span><br><span>To address this challenge, a recommended strategy is to identify key CG conformations and employ</span><br><span>backmapping methods to retrieve atomistic coordinates. Currently, rule-based methods often yield</span><br><span>suboptimal geometries and rely on energy relaxation, resulting in less-than-optimal outcomes. In</span><br><span>contrast, machine learning techniques offer higher accuracy but may lack transferability between</span><br><span>systems or be tied to specific CG mappings. In this study, we present HEroBM, a dynamic and scalable</span><br><span>method that utilizes deep equivariant graph neural networks and a hierarchical approach to achieve</span><br><span>high-resolution backmapping. HEroBM is capable of handling any type of CG mapping, providing a</span><br><span>versatile and efficient protocol for reconstructing atomistic structures with high accuracy. Grounded</span><br><span>in local principles, HEroBM spans the entire chemical space and can be applied across systems of</span><br><span>varying composition and sizes. We demonstrate the versatility of our framework through a range of</span><br><span>biological systems, including a complex real-case scenario. Here, our end-to-end backmapping approach</span><br><span>accurately generates atomistic coordinates for a G protein-coupled receptor bound to an organic small</span><br><span>molecule within a cholesterol/phospholipid bilayer. The high-fidelity HEroBM backmapping enables</span><br><span>researchers to effortlessly transition between CG and all-atom simulations, opening unprecedented</span><br><span>avenues for molecular investigations.</span></p>
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Multi-fidelity Generative Deep Learning Turbulent Flows
<p>Data sets for the two numerical examples in the paper <a href="https://arxiv.org/abs/2006.04731">Multi-fidelity Generative Deep Learning Turbulent Flows</a> as well as two pre-trained models. In this work, a novel multi-fidelity deep generative model is introduced for the surrogate modeling of high-fidelity turbulent flow fields given the solution of a computationally inexpensive but inaccurate low-fidelity solver. The resulting surrogate is able to generate physically accurate turbulent realizations at a computational cost magnitudes lower than that of a high-fidelity simulation. The deep generative model developed is a conditional invertible neural network, built with normalizing flows, with recurrent LSTM connections that allow for stable training of transient systems with high predictive accuracy. Data is provided from OpenFOAM LES simulations for turbulent flow over backwards step and flow around an array of cylinders.</p> <p>Data-set Files:</p> <ul> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/backward_step_testing.tar.gz?versionId=320a523f-0015-4ba3-8c6e-66733ab5a1af">backward_step_testing.tar.gz</a> - Backward step testing data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/backward_step_training.tar.gz?versionId=ac34ac15-973d-4fb8-8881-faa17eced69f">backward_step_training.tar.gz</a> - Backward step training data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinder_array_testing.tar.gz?versionId=ebbea725-c8b6-4338-978b-dc73f943552e">cylinder_array_testing.tar.gz</a> - Cylinder array testing data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinder_array_training.tar.gz?versionId=b0409c34-fb19-45c4-bbba-6968fdcfc4d8">cylinder_array_training.tar.gz</a> - Cylinder array training data.</li> </ul> <p>Pre-trained Models:</p> <ul> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/bstepWorkspace400.zip">bstepWorkspace400.zip</a> - Backward step pre-trained model.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinderWorkspace400.zip">cylinderWorkspace400.zip</a> - Cylinder array pre-trained model.</li> </ul> <p> </p>
Structural role for DNA ligase IV in promoting the fidelity of non-homologous end joining
<p>This repository contains the original, uncropped .tif files for gel images in Stinson et al., Nature Communications (2023). File names in the repository correspond to figure panels in the published manuscript.</p>
Virtual RHI lidar scans retrieved in high-fidelity wake vortex simulations of landing aircraft under turbulent crosswind conditions - LES Lidar Simulator (LLS)
<p>This dataset contains two types of virtual measurements of multiple pulsed lidar integrated into high-fidelity hybrid RANS-LES wake vortex simulations of a landing Airbus A340 aircraft. Simulations have been performed for four different atmospheric conditions, varying in crosswind and therefore turbulence in the atmosphere.</p> <p>The two lidar simulator types are:</p> <ul> <li>LLS: LES Lidar Simulator (no noise, Signal to Noise Ratio >> 1). The analysis is based on the Range Gate Weighting function (RWF) [based on formulations in for example [1]]. Given the assumption of no instrument noise, no spectral analysis in the frequency domain is required.</li> <li>LLSn: LES Lidar Simulator with noise (realisitic Signal to Noise Ratio). Based on formulations in [2], where non-linear low-pass spatial filters accurately model real field measurements. Both background noise and aerosol noise contained in real lidar measurements are modeled. </li> </ul> <p>Provided are the raw lidar scans in RHI format, with the position and strength of the wake vortices within each lidar scan given by a pressure-vorticity tracking algorithm from the wake vortex simulation (simulation truth, ST). Misidentifications have been removed from the provided dataset. In addition to the labels for each wake vortex lidar scan, also wind background scans are provided for the crosswind simulation cases. These background scans give a better understanding of the prevailing atmospheric condition within which the aircraft lands. </p> <p>Furthermore the evaluation of the LLS scans using the Method of Radial Velocities (RV method) [3], a state-of-the-art wake vortex characterization method for lidar scans is provided for a subset of the LLS dataset. For analysing the impact of the RWF for LLS scans, selected lidar scans are also given in a 'naive' fashion (lidar scans assuming point measurements are possible).</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>Overview</strong></h3> <p><strong>In total each dataset (LLS and LLSn) contains 8 Aircraft landing simulation with associated </strong><strong>virtual lidar RHI scans:</strong></p> <ul> <li>2x no wind</li> <li>6x crosswind (specified at height b_0)</li> <li>0.5w_0 from port direction</li> <li>0.5w_0 from starboard direction</li> <li>1.0w_0 from port direction</li> <li>1.0w_0 from starboard direction</li> <li>2.0w_0 from port direction</li> <li>2.0w_0 from starboard direction</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>The dataset has 13 folders:</strong></h3> <div> <p><strong>POS1_POS2_POS8_scans = Individual scans of various simulations as well as background wind scans (no wake vortices). </strong></p> <p>POS1_POS2: 0_0, 0_5, 1_0, 2_0 corresponds to the wind, same as POS1_POS2 below</p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> </div> <div> </div> <div>Scan naming convention: POS1_POS2_POS3_POS4_POS5_POS6_POS7.csv</div> <div> </div> <div>Example: 0_5_D_248_8_161.2201878198302_168.4201878198237.csv</div> <div> </div> <div> <ul> <li>POS1_POS2: Together they form a factor which is multiplied with the initial descend speed of the wake vortex pair, w_0. The definition for w_0 can be found in [4]. It is common for the crosswind speed to be set according to the multiples of w_0. Due to the landing of the A340 aircraft, and the logarithmic nature of the wind simulation, we set the crosswind at the b_0 altitude of the simulation. For a definition of b_0, also see [4]. For the above example, 0.5w_0 is the crosswind speed. Note that every 26 lidar positions (POS4), the direction of the crosswind changes, if there is a crosswind.</li> </ul> </div> <div> - Crosswind approaches from port side: POS4: 0-25, 52-77, 104-129, 156-181, 208-233, 260-285</div> <div> - Crosswind approaches from starboard side: POS4: 26-51, 78-103, 130-155, 182-207, 234-259, 286-311</div> <div> <ul> <li>POS3: Label indicating which part of the numerical simulation this scan belongs to (a full landing simulation consists of A-D). A: Hybrid RANS-LES, B,C,D: temporal LES (in order). For the above example, D indicates that the lidar scan was recorded during the last part of the simulation. W: Prior to the wake vortex simulations with crosswind, a background wind scan for each lidar position is recorded.</li> </ul> </div> <div> <ul> <li>POS4: Specifies the number of the lidar, implications are the longitudinal position along the glide path of the aircraft. In the packs of lidar positions described in the description of POS1_POS2, the last lidar position is the closest to the touchdown point of the aircraft, smaller lidar positions within this pack are further away (in order) - also see the lid_plane_info directory. Note that the lidar position also adjusts the spectrum of the elevation angles used (and thus the size of the lidar scan). For that see the associated virtual lidar scan raw data. In the above example we have lidar position 248, thus crosswind approaching from starboard and rather mid-way of the longitudinal glide path direction.</li> </ul> </div> <div> <ul> <li>POS5: Specifies the number of the scan for this lidar position (POS4) and simulation (POS1__POS2). In the above example this is scan number 8.</li> </ul> </div> <div> <ul> <li>POS6_POS7: Specifies the simulation time within which the scan was measured during the aircraft landing simulation (POS1__POS2). In the above example this is 161.2201878198302 s to 168.4201878198237 s.</li> </ul> </div> <div>The scans purely with wind, an no wake vortices are stored in an additional directory within conv_scans. The case '0_0' (no wind) does not require wind scans. </div> <div> </div> <div>Within the scan files, we have a four (LLS) or five (LLSn) columns:</div> <div> <ul> <li>t: Simulation time</li> <li>ELE: Elevation angle [deg] of the lidar beam</li> <li>R: Range from lidar [m]</li> <li>v_r: LOS velocity (radial velocity) along lidar beam</li> <li>snr: Signal to Noise Ratio </li> </ul> <p> </p> <p><strong>wind_POS8_scans = Simulated lidar scans of the background wind for LLS and LLSn.</strong></p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> <p>Scans simulated here are in the same format as <em>POS1_POS2_POS8_scans</em>. Wind scans for point measurement scans are found in <em>naive_scan_subset </em>- the scans with POS4 = 0 should be used if more are available.</p> <p> </p> <p><strong>naive_scan_subset = Simulated lidar scans simulating velocity point measurements.</strong></p> </div> <div> <p>The format is the same as for <em>POS1_POS2_POS8_scans</em>, with the difference of being sorted first by wind subdirectories and then lidar number (LID).</p> <p> </p> <p><strong>labels = Labels of wake vortices for the above wake vortex scans. </strong></p> </div> <div>We have 4 main files, where each file represents the targets for one wind strength 0.0w_0, 0.5w_0, 1.0w_0, 2.0w_0.</div> <div>Within the labels files, we have a multitude of columns with different data:</div> <div> <ul> <li>#Time: Simulation (not scan) time.</li> <li>y_uw : lateral position in simulation domain of upwind (port) vortex.</li> <li>z_uw : height position in simulation domain of upwind (port) vortex.</li> <li>y_dw : lateral position in simulation domain of downwind (starboard) vortex.</li> <li>z_dw : height position in simulation domain of downwind (starboard) vortex.</li> <li>G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex [m^2/s].</li> <li>G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex [m^2/s].</li> <li>cr_uw: Core radius of upwind (port) vortex (in meters).</li> <li>cr_dw: Core radius of downwind (starboard) vortex (in meters).</li> <li>uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar.</li> <li>dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar.</li> <li>uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>vortex age uw [s]: Age of upwind (port) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>vortex age dw [s]: Age of downwind (starboard) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>scan: Associated scan.</li> <li>LID: Associated lidar number.</li> </ul> <p>On top of the summarizing files for each simulation, in the subdirectory <em>individual</em>, the label for each scan is given in a separate file.</p> <p>The above gives information on the simulation truth, furthermore the file <em>labels_with_rv.csv</em> can be found here, where the following extra label columns are given for a subset of scans: [in the following Conv and Naive refer to LLS scans and point measurement scans, respectively]</p> <ul> <li>wind: Strength of crosswind, same as POS1_POS2 in <em>POS1_POS2_scans. </em>The sign corresponds to the wind direction. Negative is a crosswind from the port side of the aircraft, positive is a crosswind from the starboard side of the aircraft.</li> <li>Conv RV: 1 indicates the RV method has been evaluated for LLS scans, 0 if not.</li> <li>Naive RV: 1 indicates the RV method has been evaluated for point measurement scans, 0 if not.</li> <li>conv rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>conv rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>naive rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>naive rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>uw phi [deg]: Elevation angle to the center of the upwind (port) vortex from the simulation truth.</li> <li>uw range [m]: Range from the lidar to the center of the upwind (port) vortex from the simulation truth.</li> <li>dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>conv rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>conv rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>naive rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>naive rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>y err conv uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>y err conv dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>z err conv uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>z err conv dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>y err naive uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>y err naive dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>z err naive uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>z err naive dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>D err conv uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>D err conv dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>D err naive uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>D err naive dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>plus_minus_phi: Indicates whether a RHI lidar scan features a positive lidar scanning rate (plus), or a negative one (negative).</li> <li>conv err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on LLS scans [m^2/s]. </li> <li>conv err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on LLS scans [m^2/s]. </li> <li>naive err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on point measurement scans [m^2/s]. </li> <li>naive err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on point measurement scans [m^2/s]. </li> <li>conv err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>conv err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [deg].</li> <li>naive err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>naive err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [deg].</li> <li>conv err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [m].</li> <li>conv err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [m].</li> <li>naive err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on point measurement scans [m].</li> <li>naive err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [m].</li> </ul> <p><em>0_0_movement.csv</em> gives insight on the vertical movement on selected vortices in selected lidar scans of the 0_0 wind case. </p> <p>The following data columns exist within the file (note csv file with ; separator):</p> <ul> <li>Str_dw: Vertical movement of the downwind (starboard) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>Prt_uw: Vertical movement of the upwind (port) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>scan_code: Identfies associated lidar scan with POS3_POS4_POS5 as in <em>POS1_POS2_scans.</em></li> </ul> <p> </p> <p><strong>lid_plane_info = Additional guidance on the simulated lidars.</strong></p> <p>Each simulation case has a separate file due to minimal ground speed changes of the aircraft. </p> <p>The following data columns exist within each file:</p> <ul> <li>Index: Gives the lidar number corresponding to POS4 and LID.</li> <li>Plane Pos [x]: Gives the longitudinal position of the lidar with respect to the glide path of the aircraft in meters.</li> <li>time plane passed [s]: Gives the simulation time when the aircraft first passes the measurement plane (related to the previous column.</li> <li>height plane passed [m]: Aircraft altitude at the respective lidar positon.</li> <li>lateral position lidar from GP [m]: Lateral position of the lidar from the glide path of the aircraft.</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> </div> <h3>Additional Notes: </h3> <div> <ul> <li>Gamma 515 circulation [4]: The Gamma 515 circulation is the averaged circulation of a vortex evaluated at radii 5-15m from the vortex center. </li> </ul> </div> <div> <div> <ul> <li>Lidar scans may contain 2 or 1 vortex. In the case of 1 vortex, the other has G_515_?? set to nan. Note that the position may still available in the labels data sets, but only vortices with available G_515_?? values should be used.</li> <li>The upwind and downwind definitions should purely be taken as names, rather than physical meaning. The names are derived from the simulation, due to mirroring and other post-processing these can be misleading however.</li> </ul> </div> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> <p><strong>Funding</strong>: This dataset was generated within the mFUND <a href="https://bmdv.bund.de/SharedDocs/DE/Artikel/DG/mfund-projekte/kiwi.html">KIWI project</a> funded by the Federal Ministry for Digital and Transportation Germany and the DLR undertaking "Wetter und Disruptive Ereignisse".</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <p><strong>Acknowledgements:</strong></p> <p>The necessary RANS simulations were performed as part of the EU-funded AWIATOR by DLR's Institute of Aerodynamics and Flow Technology.</p> <p>We acknowledge Airbus for the allowance to use the RANS data.</p> <p>The LES was performed with the incompressible Navier-Stokes code MGLET [5] and kindly provided by the Technical University of Munich, Hydromechanics.</p> <p>The wake vortex simulations were computed on the high performance computer SuperMUC-NG by Leibniz-Rechenzentrum (LRZ).</p> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> </div> <div><strong>References</strong>: </div> <div> </div> <div>[1] Robey, Rachel, and Julie K. Lundquist. "Behavior and mechanisms of Doppler wind lidar error in varying stability regimes." <em>Atmospheric Measurement Techniques</em> 15.15 (2022): 4585-4622.</div> <div> </div> <div>[2] Stephan, Anton, Norman Wildmann, and Igor Smalikho. "Effectiveness of the MFAS Method for Determining the Wind Velocity Vector from Windcube 200s Lidar Measurements." <em>Atmospheric and Oceanic Optics</em> 32.5 (2019): 555-563.</div> <div> </div> <div>[3] Smalikho, Igor., et al. "Method of radial velocities for the estimation of aircraft wake vortex parameters from data measured by coherent Doppler lidar." <em>Optics Express</em> 23.19 (2015): A1194-A1207.</div> <div> </div> <div>[4] Gerz, Thomas, Frank Holzäpfel, and Denis Darracq. "Commercial aircraft wake vortices." <em>Progress in Aerospace Sciences</em> 38.3 (2002): 181-208. </div> <div> </div> <div>[5] Manhart, Michael. "A zonal grid algorithm for DNS of turbulent boundary layers." <em>Computers & fluids</em> 33.3 (2004): 435-461.</div>
A Thermogelling Organic-Inorganic Hybrid Hydrogel with Excellent Printability, Shape Fidelity and Cytocompatibility for 3D Bioprintingg
<p>Dataset for manuscript submitted for peer review</p>
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</p>
Fidel Löhle (l1211)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Fidel Löhle<br><u>musiXplora-ID</u>: l1211<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/l1211">https://musixplora.de/mxp/l1211</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1867<br><u>Sectors</u>: Kirche<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Main Place of Activity</u>: Memmingen<br><u>Other Places of Activity</u>: Waltenhausen<br><br><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Bernhard 2003a</td><td>Orgeldatenbank Bayern</td><td><a href="https://musixplora.de/mxp/5001131">5001131</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Supplementary dataset for "Magnetic recording fidelity of basalts through 3D nanotomography, 2024"
<p>This repository contains raw data and scripts needed to reproduce the results presented in "Magnetic recording fidelity of basalts through 3D nanotomography, 2024". These include slice-and-view image stacks (<a href="../api/records/11369780/draft/files/VesuviusSnVTiffStack.tif/content" target="_blank" rel="noopener noreferrer">VesuviusSnVTiffStack.tif</a> for the Vesuvius dataset and <a href="../api/records/11369780/draft/files/HeklaSnVTiffStack.tif/content" target="_blank" rel="noopener noreferrer">HeklaSnVTiffStack.tif</a> for the Hekla Volume) for the samples discussed in the manuscript. These were used to generate 3D meshes of magnetite grains in the volume using the methodology described in the manuscript. <a href="../api/records/11369780/draft/files/Individual%20Meshes%20Vesuvius.7z/content" target="_blank" rel="noopener noreferrer">Individual Meshes Vesuvius.7z</a> and <a href="../api/records/11369780/draft/files/Individual%20Meshes%20Hekla.7z/content" target="_blank" rel="noopener noreferrer">Individual Meshes Hekla.7z</a> contain the individual 3D mesh .pat files, while <a href="../api/records/11369780/draft/files/Hekla%20Full%20Volume.stl/content" target="_blank" rel="noopener noreferrer">Hekla Full Volume.stl</a> and <a href="../api/records/11369780/draft/files/Vesuvius%20Full%20Volume.stl/content" target="_blank" rel="noopener noreferrer">Vesuvius Full Volume.stl</a> show full 3D representations of the studied volumes. The individual mesh files can be used as geometry inputs for micromagnetic simulations using the MERRILL suite . Example MERRILL scripts are also included, with <a href="../api/records/11369780/draft/files/LEM_StateMerrilScript.merrill/content" target="_blank" rel="noopener noreferrer">LEM_StateMerrilScript.merrill</a> showing an example of a script used to determine the local energy minimum (LEM) state of a magnetic grain and <a href="../api/records/11369780/draft/files/NEB_Merril_Script.merrill/content" target="_blank" rel="noopener noreferrer">NEB_Merril_Script.merrill</a> showing a script to determine the energy barriers between LEM states used in the calculation of relaxation times. Finally ".csv" files containing grain metrics are also included for both of the studied samples ( <a href="../api/records/11369780/draft/files/HeklaGrainMetrics.csv/content" target="_blank" rel="noopener noreferrer">HeklaGrainMetrics.csv</a> and <a href="../api/records/11369780/draft/files/VesuviusGrainMetrics.csv/content" target="_blank" rel="noopener noreferrer">VesuviusGrainMetrics.csv</a> ). These contain information on the size of the individual particles, their morphology, the LEM states they support and the energy barries from the NEB calculation.</p>
Data for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect
<p>Dataset for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect, in final revision for Nature Communications.</p> <p>Includes two files - one behavioral data from uniquely identified worker bumblebees, and the second containing metadata for the colonies from which these data were generated (including experimental treatments, locations, sizes, etc.).</p>
Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability: Subjective fidelity study data
<p>Supplementary Material to the Paper: <em>Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability</em></p> <p>This folder contains all collected data and scripts that were used to analyze the subjective fidelity study.</p>
[Source code and Dataset] FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes
<p>FrozenQubits is a novel quantum divide-and-conquer approach that leverages the insight about most real-world applications having power-law graphs to boost the fidelity of QAOA applications. In FrozenQubits, we freeze some of the nodes with the highest degree of connectivity. This drops the edges connected to these nodes resulting in a circuit with fewer CNOTs and lower depth.</p>
Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration
<p>This repository contains a dataset used for the article "<em>Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration</em>" (<a href="https://arxiv.org/abs/2210.03484">arXiv:2210.03484</a>). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.</p> <p>In the dataset we have <em>train_x </em>and <em>train_obj </em>numpy arrays with dimensions <em>(n,5)</em> and<em> (n,3)</em>, respectively. Here <em>n</em> is the number of FBPIC simulations. The five columns in <em>train_x </em>are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the <em>train_obj </em>are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.</p> <p>The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
Dataset: Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
<p>Direct numerical simulation (DNS) data for 2D forced homogeneous isotropic turbulence (FHIT) and 2D Rayleigh-Benard Convection (RBC). This data is used to discover subgrid-scale (SGS) closures for momentum and heat fluxes.</p> <p>Paper: "<strong>Learning Closed-Form Equations for Subgrid-Scale Closures From High-Fidelity Data: Promises and Challenges</strong>" <br> <a href="https://doi.org/10.1029/2023MS003874">https://doi.org/10.1029/2023MS003874</a></p>
Supplementary data: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping
<p>--- Data supplement---</p> <p>--- Title: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping</p> <p>The zip file contains the following directories and subdirectories. The files, recommended software, calling code and relevance to the main manuscript (preprint found at https://doi.org/10.1101/2023.02.20.529230) are included in the notes below:</p> <p>(i) “STL files” – Includes .stl file formats of the Expansion Microscopy microplate components. These files can be opened with any CAD software (e.g. Fusion 360) or any opensource 3D printer slicer programme (e.g. Chitubox v1.9.5)</p> <p>(ii) “Image alignment & distortion analysis code” – Directory containing the ImageJ macros and custom-written Python scripts for the analysis pipeline described in the main manuscript, from image scaling and alignment to for distortion plotting and RMS Error analysis and plotting. Subdirectories include:</p> <ul> <li>a.“Distortion analysis and RMSE plotting code” – Directory contains custom-written Python scripts for updated distortion analysis, plotting, RMS error calculation, and code for combining the plots. Each .py file can be run with any Python distribution. Dependency libraries and modules (all opensource) include numpy, os, cv2, skimage, and tifffile</li> <li>b.“Basic Align Macro.ijm” – ImageJ macro for basic image alignment of pre- and post-Expansion Microscopy images. Refer to <a href="https://imagej.nih.gov/ij/developer/macro/macros.html#tools">https://imagej.nih.gov/ij/developer/macro/macros.html#tools</a> on how to install and run ImageJ macros.</li> </ul> <p> </p> <p>(iii)“Example data and analysis scripts” – Directory containing example datasets and worked examples of analysis. Subdirectories include:</p> <ul> <li>a.“Single-channel_HeLa_cells”. Worked example of single-colour dataset of a cluster of HeLa cells stained with NHS-AZ488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format). This can be called in using a Python code similar to: numpy.load("file name")</li> <li>b.“Multiplexed_Drosophila_wing”. Worked example of two-colour dataset of Drosophila fly wing tissue images stained with NHS-Alexa647 and anti-E-cad-GFP/Alexa488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format).</li> <li>c.“Multiplexed_HeLa_cells”. Worked example of two-colour dataset of cultured HeLa cell images stained with NHS-AZ488 and antibodies. Each folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format): <ul> <li>I.“KDELAlexa594_NHSAZ488” – antibody staining against KDEL</li> <li>II.“NUP98Alexa594_NHSAZ488” – antibody staining against Nups98</li> </ul> </li> </ul> <p> </p> <ul> <li>d.“Distortion Correction”. Folder contains two subfolders of output images from distortion corrections using the Linear Stack Alignment with SIFT plugin in ImageJ v1.54f. Each example consists of a pre- and post-Expansion image file, and the ‘corrected’ image file generated with 5, 10, and 50 voxel B-spline sampling.</li> </ul> <p> </p> <p>These files are placed in public domain under Creative Commons license CC BY-ND 4.0</p>
Testing the Fidelity of Paleopole Determinations from Multidirectionally Magnetized Lunar Crustal Anomaly Source Bodies
<p>Code and data for "Testing the Fidelity of Paleopole Determinations from Multidirectionally Magnetized Lunar Crustal Anomaly Source Bodies ". See readme.txt</p>
High-fidelity simulation of the effects of street trees, green roofs and green walls on the distribution of thermal exposure in Prague-Dejvice
<p>Archive with PALM simulation results. All data were used in paper <a href="https://doi.org/10.1016/j.buildenv.2022.109484">https://doi.org/10.1016/j.buildenv.2022.109484</a></p>
Fig. 3 in Black Tern Nest-Site Fidelity In An Unstable Habitat: A Preliminary Study
Fig. 3. The distribution of Black Tern colonies at the northern part of Kaniv water reservoir. The only colonies shown here are those where the returns of birds, which had been marked in 2012–2019, were recorded.
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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.