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136 results for “Diffusion models”
Science ready spectra, their best-fitting models and results of Jeans axisymmetric modelling described in the research paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al.
<p>This package contains data presented in the paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al. (2021 Nature Astronomy in press). The dataset can be used to reproduce Figures 3 and 4 from the main manuscript and Extended Data Figures 1, 2, 4, 5 from the Supplementary Information.</p> <p>(1) Python scripts and data points required to reproduce Figure 4 in the manuscript and Extended Data Figure 5 from the Supplementary Information. The data and scripts are presented in a combined .zip archive for both figures.</p> <p>(2) One-dimensional spectra extracted within 1 half-light radius from long-slit Binospec spectra and multi-wavelength far-UV-to-near-IR broadband spectral energy distributions (SEDs) assembled from the photometric measurements extracted within the same aperture for 11 galaxies from the main sample (9 in the Coma cluster and 2 in the Abell 2147 cluster) and 5 galaxies from the supplementary (auxiliary) list. The spectra and SEDs are accompanied with their best-fitting stellar population models and parameters determined by the NBursts+phot algorithm: radial velocity, velocity dispersion, truncation age, final stellar metallicity. The templates are MILES-based models with self-consistent chemical evolution presented in Grishin et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019arXiv190913460G/abstract). The filenames contain the coefficient for galactic winds and the mass fraction of stars in the final starburst, e.g. _l15_60 means lambda=1.5, SSP_frac=60 per cent. The files are presented as binary FITS tables with the fields annotated using unified content descriptors (UCDs) from the list established by the International Virtual Observatory Alliance and physical units where applicable.</p> <p>(3) Two-dimensional profiles of internal kinematics (radial velocity and velocity dispersion) and stellar population properties (truncation age and final stellar metallicity) derived from the analysis of long-slit Binospec spectra for 12 galaxies after adaptive binning, 11 from the main sample and GMP3016 in the Coma cluster from the supplementary sample; best-fitting Jeans axisymmetric models without adaptive binning, i.e. full profiles along the slit. The data are presented in binary FITS tables in the two FITS extensions, one for the profiles derived from the corresponding spectra and the second one for dynamical models.</p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
Datasets for manuscript - Dirichlet diffusion score model for biological sequence generation.
<p>This repository holds the trained Dirichlet Diffusion Score models for various datasets.</p> <p><strong>best_models.tar.gz</strong></p> <p>It also contains all input data required to train your own models with scripts provided via <a href="https://github.com/jzhoulab/ddsm">github repository</a>.</p> <p><strong>data.tar.gz</strong></p> <p>This archive contains the following folders: </p> <ul> <li><strong>satnet_sudoku </strong>contains dataset with sudoku examples which we used for evaluation of sudoku model.</li> <li><strong>promoter_design</strong> contains dataset used for training promoter design model as well as Sei model weights. Please, read provided readme file before using it for training scripts. </li> </ul>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
The Plasma Distribution in Saturn's Inner Magnetosphere from 2.4 to 10 Rs: A Diffusive Equilibrium Model
<p>This is the supporting data set for the paper by the same title published in AGU JGR Space Physics, <a href="https://doi.org/10.1029/2019JA027545">10.1029/2019JA027545</a></p> <p>Abstract:</p> <p>Electron density measurements have been obtained by the Cassini Radio and Plasma Wave Science (RPWS) instrument covering the period from 30 June, 2004 to 19 April, 2017, spanning latitudes up to ~30<sup>o </sup>and <em>L</em> values from 2.4 to 10. Near the F ring, electron densities are derived from RPWS measurements of electron plasma oscillations at high latitudes and from the Langmuir Probe (RPWS/LP) sweep data at low latitudes. The electron density measurements from the ring-grazing orbits, beginning in December 2016, have made it possible to extend the work of a previous diffusive equilibrium model to include the distribution of the ring plasma. Beyond the ring-grazing orbits, the densities are derived from RPWS measurements of the upper hybrid resonance frequency. These density measurements are used to anchor the fit of an expanded diffusive equilibrium density model for a two-species plasma consisting of water group and hydrogen ions in Saturn’s inner magnetosphere. Density contour plots for the two ion species and the electrons are presented. The distribution of the derived plasma densities is consistent with two primary sources, the Enceladus plumes and the extended ring atmosphere. There is also an indication of a weaker plasma source at Dione. In the region just outside the A Ring and in the region including the Enceladus orbit, the diffusive equilibrium model also clearly shows the expansion of lighter ions and electrons to higher latitudes along the magnetic field lines with evidence of a weaker plasma expansion between the orbits of Tethys and Dione.</p>
Data Files for PyGDSM: Python interface to the Global Diffuse Sky Model
<p>HDF5 data files for PyGDSM: Python interface to Global Diffuse Sky Models</p> <p>PyGDSM is a Python interface for the Global Diffuse Sky Models (GDSM ascl:1011.010). GDSM are models of diffuse galactic radio emission, constructed from a variety of all-sky surveys spanning the radio band (e.g. Haslam and WMAP). PyGDSM uses the Global Sky Model (GSM2008) of <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2966.2008.13376.x/abstract">Oliveira-Costa et. al., (2008)</a>, <a href="http://arxiv.org/abs/1605.04920">Zheng et. al., (2016)</a> model GSM2016, and <a href="https://lda10g.alliance.unm.edu/LWA1LowFrequencySkySurvey/">LWA1 Low Frequency Sky Model</a> (LFSM). The PyGDSM module provides visualization utilities, file output in FITS format, and the ability to generate observed skies for a given location and date. PyGDSM requires <a href="https://healpy.readthedocs.org/en/latest/">Healpy</a>, PyEphem (ascl:1112.014), and AstroPy (ascl:1304.002).</p>
Data from: Sleep, inflammation, and cognitive behavior of aged wild-type mice subjected to diffuse brain injury and aged 3xTg-AD mice as a model of Alzheimer's disease
<p>Identifying differential responses between sexes following traumatic brain injury (TBI) can elucidate the mechanisms behind disease pathology. Peripheral and central inflammation in the pathophysiology of TBI can increase sleep in male rodents, but this remains untested in females. We hypothesized that diffuse TBI would increase inflammation and sleep in males more so than in females. Diffuse TBI was induced in C57BL/6J mice and serial blood samples were collected (baseline, 1, 5, 7 days post‐injury [DPI]) to quantify peripheral immune cell populations and sleep regulatory cytokines. Brains and spleens were harvested at 7DPI to quantify central and peripheral immune cells, respectively. Mixed‐effects regression models were used for data analysis. Female TBI mice had 77%–124% higher IL‐6 levels than male TBI mice at 1 and 5DPI, whereas IL‐1β and TNF‐α levels were similar between sexes at all timepoints. Despite baseline sex differences in blood‐measured Ly6Chigh monocytes (females had 40% more than males), TBI reduced monocytes by 67% in TBI mice at 1DPI. Male TBI mice had 31%–33% more blood‐measured and 31% more spleen‐measured Ly6G+ neutrophils than female TBI mice at 1 and 5DPI, and 7DPI, respectively. Compared with sham, TBI increased sleep in both sexes during the first light and dark cycles. Male TBI mice slept 11%–17% more than female TBI mice, depending on the cycle. Thus, sex and TBI interactions may alter the peripheral inflammation profile and sleep patterns, which might explain discrepancies in disease progression based on sex.</p>
Synthetic Out-Of-Vocabulary IAM dataset with Latent Diffusion Models
<p>SyntheticHTR: Handwritten Text Image Synthesis based on Latent Diffusion Models</p>
Model checkpoints for "SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models"
<p>Checkpoints for all SEEDS models in the paper <a href="https://arxiv.org/abs/2306.14066" rel="nofollow">https://arxiv.org/abs/2306.14066</a>, including all SEEDS-GEE and SEEDS-GPP models in the main manuscript, and the additional models trained in the Supplemental Material.</p> <p>Checkpoint naming convention:</p> <ul> <li><code>gee_c2_s7</code>: SEEDS-GEE trained conditioning on 2 seeds for 7-day lead time.</li> <li><code>gpp_c2_s7_g3_r4</code>: SEEDS-GPP trained conditioning on 2 seeds for 7-day leadtime, where the label mixture is 3 GEFS members and 4 ERA reanalyses.</li> </ul>
Stimuli from: AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures
<p>Stimuli generated using a Diffusion Model from the paper "AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures".</p> <p>Please cite as:<br><span>Ciupinska, K.; Marchesi, S.; Abbo, G. A.; Belpaeme, T. and Wykowska, A. (2024). <strong>AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures</strong>. In <em>Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: AWAI</em>; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 1436-1443. DOI: 10.5220/0012596400003636</span></p>
Vesicle and reaction-diffusion hybrid modeling with STEPS
<p>This repository, a snapshot from Github public repository <a href="https://github.com/CNS-OIST/STEPS_Validation/tree/main/vesicles">https://github.com/CNS-OIST/STEPS_Validation/vesicles</a>, contains all data and modeling scripts to reproduce the data presented in: </p> <p>Iain Hepburn, Jules Lallouette, Weiliang Chen, Andrew R. Gallimore, Sarah Y. Nagasawa-Soeda, Erik De Schutter (2024) Vesicle and reaction-diffusion hybrid modeling with STEPS. Communications Biology 7(1):573. doi:10.1038/s42003-024-06276-5</p> <p>The repository contains README files with instruction for how to run the model scripts and reproduce the figures.</p> <p>The work presented is based on the simulator STochastic Engine for Pathway Simulation (STEPS) version 5.0.1, which is publicly availble from: https://github.com/CNS-OIST/STEPS/releases/tag/5.0.1 </p> <p> </p>
Modeled data related to the article "Diffusive Wave Models for Operational Forecasting of Channel Routing at Continental Scale"
<p>The data holder contains modeled data on water level and discharge from some test cases.</p>
KRAgamma cosmic-ray diffusion models
<p>These files contain the "KRAgamma" models for the the diffuse gamma-ray maps due to to pi0, Inverse Compton and Bremsstrahlung emission associated to the Galactic cosmic-ray population.</p> <p>The Galactic cosmic-ray sea is propagated in the Galaxy with the DRAGON code, according to the transport setup described in arXiv:1411.7623.</p> <p>The properties of the multi-TeV gamma-ray emission of the KRAgamma models are described in 1504.00227.</p>
Data set related to the manuscript "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model". XYZ files giving the particles and bulk positions in the lattices are also provided.</p>
A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides
<p>In multi-cellular organisms, cells and tissues coordinate biochemical signal propagation across length scales spanning micrometres to metres. Designing synthetic materials with similar capacities for coordinated signal propagation could allow these systems to adaptively regulate themselves across space and over time. Here, we combine ideas from cell signalling and electronic circuitry to propose a biochemical waveguide that transmits information in the form of a concentration of a DNA species on a directed path. The waveguide could be seamlessly integrated into a soft material because there is virtually no difference between the chemical or physical properties of the waveguide and the material it is embedded within. We propose the design of DNA strand displacement reactions to construct the system and, using reaction-diffusion models, identify kinetic and diffusive parameters that enable super-diffusive transport of DNA species via autocatalysis. Finally, to support experimental waveguide implementation, we propose a sink reaction and spatially inhomogeneous DNA concentrations that could mitigate the spurious amplification of an autocatalyst within the waveguide, allowing for controlled waveguide triggering. Chemical waveguides could facilitate the design of synthetic biomaterials with distributed sensing machinery integrated throughout their structure and enable coordinated self-regulating programmes triggered by changing environmental conditions.</p>
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>This repository (and several sub-repositories) contains the data for the manuscript "His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models"</p> <p>The current repository contains the de-identified metadata of WSIs. Additionally, it contains Supplementary Data S1-S7.</p> <p>Due to the large size of WSIs, the archives have been divided into several parts to satisfy the size limit of Zenodo.</p> <p>HMU-C dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12636965">https://zenodo.org/doi/10.5281/zenodo.12636965</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12705912">https://zenodo.org/doi/10.5281/zenodo.12705912</a></p> <p>HMU-1st dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12710399">https://zenodo.org/doi/10.5281/zenodo.12710399</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12723825">https://zenodo.org/doi/10.5281/zenodo.12723825</a></p> <p>part c: <a href="../doi/10.5281/zenodo.12724507">https://zenodo.org/doi/10.5281/zenodo.12724507</a></p> <p>part d: <a href="../doi/10.5281/zenodo.12726785">https://zenodo.org/doi/10.5281/zenodo.12726785</a></p> <p>Please fully download all the parts and concatenate them before extraction.</p> <p>Supplementary Data S1-S7:</p> <p><a href="https://zenodo.org/doi/10.5281/zenodo.16763510">https://zenodo.org/doi/10.5281/zenodo.16763510</a></p>
The output data of the 1D Venusian chemistry-diffusion model of Dai et al. (2024) and the adopted chemical network
<p>To use these data, please cite the paper: Dai et al. (2024, doi: 10.1051/0004-6361/202450552) </p> <p>Supplemental_Tables_for_Dai_et_al_2024.pdf: the chemical network adopted in the model</p> <p>Nominal.txt: the chemical network adopted in the model</p> <p>modify_chem.py: the additional adjustments of the reactions</p> <p>Nominal_Bkzz_SO2.vul and A_Dkzz_SO2.vul: the output data of the nominal model and model A, respectively</p> <p>reading_data.py: the methods to read the output files</p> <p> </p> <p>*Errata: </p> <p>1) R296 in Supplemental Table: "1×10^7+0.05n_atm" should have been "1×10^17+0.05n_atm"</p> <p>2) R329 in Supplemental Table: should have been removed</p> <p>The errata do not affect the results of this study.</p>
Inverse Design of Metamaterials with Manufacturing-Aware Spectrum-to-Shape Diffusion Models
<p>The dataset includes detailed information on the MIM tri-layer metamaterial structures designed and used for training the DiffMeta framework. Specifically, it contains 60000 data:</p> <p>Structural Data: Detailed geometric patterns and composition parameters of the designed MIM tri-layer metamaterial structures.</p> <p>Spectral Data: Spectral measurements on MIM tri-layer metamaterial structures, including emissivity, reflectivity and transmittance spectra across a range of wavelengths.<br><br>The dataset is meticulously organized to facilitate the replication of our study and support further research in the field of metamaterial design. </p>
Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads
<p>This map contains the data used in the study 'Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads'. In this study a new method is presented to estimate average diffuse aquifer recharge of water table aquifers in temperate climates using time series analysis of water table level fluctuations. Recharge is estimated from time series models fitted to observed heads under the additional constraint that the seasonal harmonic of the observed head is reproduced as the sum of the transformed seasonal harmonics present in precipitation, evaporation, and pumping. The method is applied to measured heads obtained from piezometers situated on and around the ice-pushed sand ridge of Salland in the Netherlands. Results are compared with recharge estimates based on the saturated zone chloride mass balance.</p> <p>The data are ascii text files saved in the following compressed maps:<br> - Groundwater_chloride_concentration.zip<br> - Groundwater_head_time_series.zip<br> - Makkink_reference_evaporation_time_series.zip<br> - Measured_precipitation_time_series.zip<br> - Precipitation_chloride_concentrations.zip<br> - Pumping_time_series.zip</p> <p>For further details, please refer to the readme files contained in each submap.</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.