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Dataset results
236 results for “Modeling Methods”
Method to Improve Workflow Net Decomposition for Process Model Repair - experiments
<p>This repository contains data that was used to carry out experiments as well as the results of these experiments.</p> <p>File names are presented in the following format: LM2-[repair method]-[b/f]-[number of experiment], where:<br> - repair method may take values "Greedy" or "Smart". "Greedy" means that the repair method of the model was greedy algorithm working with maximal decomposition. "Smart" means that the repair method was greedy algorithm as well, with the difference of decomposition method being the developed one.<br> - "b" (broken) means that the model has not undergone repair. "f" (fixed) means that the file presents a repaired model (the one which fits the initial log perfectly).<br> - number of experiment ranging from 1 to 10.</p> <p>This repository also contains the following files:<br> - LM2-CL.xes - the initial LM2 model log;<br> - LM2-CM.pnml - the initial LM2 model;<br> - LM2-Greedy-data.txt - auto-generated measurements of greedy approach performance;<br> - LM2-Smart-data.txt - auto-generated measurements of smart (developed) approach perfomance.</p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Code and data archive to accompany "A derivative-free optimisation method for global ocean biogeochemical models", Oliver et. al. 2021
<p>This archive is to accompany the article:</p> <p>A derivative-free optimisation method for global ocean biogeochemical models,<br> Sophy Oliver, Coralia Cartis, Iris Kriest, Simon Tett, and Samar Khatiwala.</p> <p>The optimisation framework used in this study can be found here: https://doi.org/10.5281/zenodo.5517610</p> <p>The original source code of MOPS were from the Supplement of Kriest et al. (2017).<br> The most recent TMM source code is available at https://github.com/samarkhatiwala/tmm.</p> <p>In this archive:</p> <p>Supplement/Configurations/OxfordMOPS_Configs contains:<br> - ReadOnlyFiles (Files and Code specifically used to run the global ocean biogeochemical model MOPS model with<br> the Transport Matrix Method, which have been edited to differ from the versions downloaded from the sources above.)<br> - RunCode (runscripts to run the MOPS model with the TMM)<br> - TWIN_Configs (JSON files required by each optimisation experiment carried out).</p> <p>Supplement/OxfordMOPS_EXP contains data for each iteration of all optimisation experiments carried out.</p> <p>Supplement/OPTCLIMSO_PlottingScripts contains MATLAB plotting scripts used to create results figures of these experiments.</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>
A Mixed-Flux-Based Nodal Discontinuous Galerkin Method for 3D Dynamic Rupture Modeling
<p>This repository contains data produced by a mixed-flux-based discontinuous Galerkin method for 3D dynamic rupture modeling, using the software DRDG3D (<a href="https://github.com/wqseis/drdg3d">https://github.com/wqseis/drdg3d</a>). Input scripts for the SCEC/USGS dynamic rupture benchmark validation problems (<a href="https://strike.scec.org/cvws">https://strike.scec.org/cvws</a>) and other cases are hosted on DRDG3D's GitHub page. The preprint is published at ESS Open Archive (DOI: <a href="http://doi.org/10.1002/essoar.10512657.1">10.1002/essoar.10512657.1</a>).</p>
Numerical study of the one-dimensional Holstein model using the momentum-space hierarchical equations of motion method
<p>Data on the finite-temperature current-current correlation function of the one-dimensional Holstein model. Data are obtained using the newly developed momentum-space hierarchical equations of motion (HEOM) method. Details on the method development, as well as on the model parameters, will be given as a supplementary material to a journal publication that will be deposited on arXiv. Folders Regime* contain temporal evolution of the current-current correlation function (j_j_real_time.txt), diffusion constant (diffusion_constant.txt), diffusion exponent (diffusion_exponent.txt), and the electron's spread (delta_x.txt). They also contain frequency profiles of the Fourier transformed current-current correlation function (j_j_real_frequency.txt) and dynamical mobility (dynamical_mobility.txt).</p>
Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods
<p>Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.</p> <p> </p> <p><strong>Data from two studies</strong></p> <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol> <p> </p> <p><strong>Folders and Files</strong></p> <p><strong>valve-2D.zip </strong>is the folder with data from the idealized models of vertical and curved 2D valves:</p> <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>inflow-outflow-3D.zip </strong>is the folder with data from the inflow-outflow model in MPS:</p> <ul> <li>Fluid physical properties of water <ul> <li><span>\(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\)</span></li> </ul> </li> <li>Pipes of length <span>\(L=0.15m\)</span>: <ul> <li>circular section of diameter <span>\(D=0.1m\)</span>.</li> <li>square section of sides <span>\(S=0.1m\)</span>.</li> </ul> </li> <li>Constante pressure variation (<span>\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)</span>) between inflow and outflow: <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation (<span>\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)</span>) between inflow and outflow <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>References</strong></p> <p><a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a></p> <p><a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a></p> <p><a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a></p> <p><a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a></p> <p><a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a></p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
Open the record for dataset details and reuse information.
Population models used in: Method to assess potential magnitude of terrestrial European avian population reductions from ingestion of lead ammunition
Open the record for dataset details and reuse information.
Data from: Collection methods and distribution modeling for Strepsiptera in the United States
Open the record for dataset details and reuse information.
A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos
<p>This dataset was used on the paper "A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos" for the comparison of three of the most common background modelling methods. The objective was to determine which of the models would be a better fit for the videos we had available at the time.</p> <p>Images are separated into folders, each corresponding to one of the videos used. To understand the naming convention, you can check <a href="https://arxiv.org/abs/1810.02835">the paper</a>, available at arXiv.</p>
Benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model"
<p>This repository contains all benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model". It is safe to download these samples.</p>
The observed data used in paper titled "An analytic method for calculating parameters of the van Genuchten model for soil water retention curve"
<p>In the file, the 46 soil samples from UNSODA were used to test the proposed method of estimating the parameters of VG model, including the physical and hydraulic properties data, PSD, SWRC, saturated hydraulic conductivity, porosity and saturated water content. Also, this data file includes some process data and results data, <em>D</em><sub><em>i</em>, psd</sub>, predicted <em>K<sub>s</sub></em> , the data relate to <em>a </em>and <em>n </em>and sensitivity analysis.</p>
Supporting data for "A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models"
<p>Dataset necessary to reproduce the results and analyses presented in the paper:</p> <p>Gourgue, O., van Belzen, J., Schwarz, C., Bouma, T.J., van de Koppel, J. & Temmerman, S. (2020) A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models, Journal of Advances in Modeling Earth Systems, submitted.</p> <p>The dataset contains:</p> <ul> <li>Process-based model simulations, including their input files and the Python scripts to generate them (pre-processing), as well as the output files and Python scripts to post-process them.</li> <li>Flume experiment data processed for the model calibration.</li> <li>Python scripts to generate the figures and tables of the manuscript.</li> </ul>
Data for GMD article: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4"
<p>Dataset for the publication by Nina Črnivec and Bernhard Mayer: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4" submitted to Geoscientific Model Development in 2020.</p> <p>The repository contains data for stratocumulus, cirrus and cumulonimbus cloud case studies. It also contains MYSTIC benchmark radiation data (including atmosphering heating rate and net surface flux) for the aforementioned cloud cases. See README for additional information and description of data files.</p>
Model Age Derivation of Large Martian Impact Craters, using automatic crater counting methods / Dataset
<ul> <li>"counting_area" folder: shapefiles of the mapped ejecta layers considered in this study</li> <li>"scc" folder: .scc files readable on CraterStats listing the size and location of craters detected by our CDA and recognized as primaries by the ASCI. The counting area considered for each crater slightly vary from the area indicated in the shapefile due to the removal of Thiessen polygons associated to secondary craters by the ASCI.</li> </ul> <p>The ASCI code and toolbox implementable to ESRI ArcGIS (10.6) is discoverable here: https://github.com/curtin-crater-detection/secondary-crater-removal<br> </p>
A quantitative method to calibrate the SWAN wave model - pre-processed model outputs
<p>The pre-processed MAT files for the paper 'A quantitative method to calibrate the SWAN wave model'</p>
Impact of Different Nesting Methods on the Simulation of a Severe Convective Event Over South Korea Using the Weather Research and Forecasting Model
<p>The data from various platforms (NCEP FNL, TRMM, ERA5, AWS) and WRF Model output utilised to generate the figures in the current study (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JD033084) are available at this Zenodo data repository.</p>
Data from: How far can I extrapolate my species distribution model? Exploring Shape, a novel method
<p>Species distribution and ecological niche models (hereafter SDMs) are popular tools with broad applications in ecology, biodiversity conservation, and environmental science. Many SDM applications require projecting models in environmental conditions non-analog to those used for model training (extrapolation), giving predictions that may be statistically unsupported and biologically meaningless. We introduce a novel method, Shape, a model-agnostic approach that calculates the extrapolation degree for a given projection data point by its multivariate distance to the nearest training data point. Such distances are relativized by a factor that reflects the dispersion of the training data in environmental space. Distinct from other approaches, Shape incorporates an adjustable threshold to control the binary discrimination between acceptable and unacceptable extrapolation degrees. We compared Shape's performance to five extrapolation metrics based on their ability to detect analog environmental conditions in environmental space and improve SDMs suitability predictions. To do so, we used 760 virtual species to define different modeling conditions determined by species niche tolerance, distribution equilibrium condition, sample size, and algorithm. All algorithms had trouble predicting species niches. However, we found a substantial improvement in model predictions when model projections were truncated independently of extrapolation metrics. Shape's performance was dependent on extrapolation threshold used to truncate models. Because of this versatility, our approach showed similar or better performance than the previous approaches and could better deal with all modeling conditions and algorithms. Our extrapolation metric is simple to interpret, captures the complex shapes of the data in environmental space, and can use any extrapolation threshold to define whether model predictions are retained based on the extrapolation degrees. These properties make this approach more broadly applicable than existing methods for creating and applying SDMs. We hope this method and accompanying tools support modelers to explore, detect, and reduce extrapolation errors to achieve more reliable models.</p>
Induced pluripotent stem cell-derived cardiomyocyte in vitro models: tissue fabrication protocols, assessment methods, and quantitative maturation metrics for benchmarking progress
<p>The advent of human induced pluripotent stem cells (hiPSCs) and techniques to differentiate cardiomyocytes from them has opened a viable path to creating <em>in vitro</em> models of normal and diseased hearts, accelerating more predictive drug screening and therapeutic strategies for cardiac pathologies. Currently, hiPSC-derived cardiomyocytes (hiPSC-CMs) are more similar to fetal than adult cardiomyocytes, leading many in the field to explore approaches to enhance cell and tissue maturation. There are over 2,000 studies utilizing hiPSC-CMs in models composed of various combinations of cell and extracellular matrix components, using a plethora of differentiation protocols, culture formats, and methods for quantifying cardiomyocyte function. To assess the current state of this rapidly growing area, we systematically analyzed 300 studies using hiPSC-CM models for their selection of hiPSC lines, hiPSC-CM differentiation protocols, types of <em>in vitro </em>models, maturation techniques, and metrics used to assess cardiomyocyte functionality and maturity. Here, we provide the data compiled from our analysis of these papers so others in the field can utilize it to inform their research.</p> <p>Based on this analysis, we highlight the diversity of, and current trends in, <em>in vitro</em> model designs and highlight the most common and promising practices for functional assessments. We further analyzed outputs spanning structural maturity, contractile function, electrophysiology, and gene expression and note field-wide improvements over time. Finally, we observe that a persistent lack of coordination amongst investigators is limiting the field's ability to benchmark and advance hiPSC-CM function against previous studies. We discuss opportunities to collectively pursue the common goal of hiPSC-CM model development, maturation, and assessment that we believe are critical to drive the entire community forward in engineering mature cardiac tissue.</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.