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Dataset results
994 results for “2d”
Dataset of "Towards 2D van der Waals Entropy Mixture MX2 (M=Mo,W; X=S,Se,Te) for Hydrogen Evolution Electrocatalysis"
<p>High-entropy alloys have emerged as a class of materials, offering unique properties due to their irregular and randomized arrangement of multiple elements in an ordered lattice. This concept has been extended to two-dimensional (2D) van der Waals materials, including transition metal dichalcogenides (TMD), which exhibit promising applications in electrocatalysis. In this work, we have explored the synthesis of entropy mixture crystals (TMDmix) involved the chemical vapor transport of five individual elements, Mo and W as metal elements, S, Se, and Te as chalcogenide elements, resulting in a crystalline structure with a controlled composition Mo0.56W0.44(S0.33Se0.35Te0.32)2, with an estimated ΔSmix of 0.96R. When observed along the [001] zone axis, STEM HAADF images indicate the presence of the different crystal phases of the 2D TMDs (1T, 2H, and 3R). Our findings demonstrate the potential of the entropy TMDmix materials as catalysts for the hydrogen evolution reaction, as an alternative to noble metal-based catalysts. To maximize the potential of TMDmix, we chose the chemical exfoliation with the resulting material being subdivided into size groups, big and small according to their lateral size. In acidic medium, the lowest overpotential of 127 mV and Tafel slope of 79 mV/dec were obtained for the exfoliated sample with a small lateral size (exf-TMDsmall).</p>
Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
iPlacenta: hIPSC placenta-on-a-chip RNAseq data from 3D vs 2D, day 0 vs day 4 differentiation
<p>RNAseq data from hIPSC dervived trophoblasts seeded in 3D (OrganoPlate) or 2D surface at day 0 or day 4 differentiation. </p> <p>Description of file names found below</p> <table> <tbody> <tr> <td> <p><strong>SampleID/File name</strong></p> </td> <td> <p><strong>Condition- Differentiation day</strong></p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-1</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-2</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D0-3</p> </td> <td> <p>2D-Day0</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-4</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-5</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-2D-D4-6</p> </td> <td> <p>2D-Day4</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-7</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-8</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D0-9</p> </td> <td> <p>3D-Day0</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-10</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-11</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-12</p> </td> <td> <p>3D-Day4</p> </td> </tr> <tr> <td> <p>iPSC-THB-3D-D4-13</p> </td> <td> <p>3D-Day4</p> </td> </tr> </tbody> </table>
SpatialCalibration MR 2D Sagittal
<p>Included are:</p> <ul> <li>vendor-reconstructed DICOM images in NIfTI format (dicom_as_nifti.nii)</li> <li>the scanner raw MR data (meas_MID00749_FID151779_t2_tse_sag.dat)</li> <li>the raw MR data in h5 format, converted using siemens_to_ismrmrd (meas_MID00749_FID151779_t2_tse_sag.h5)</li> <li>the MR data reconstructed with SIRF in .h5 format (SIRF_recon.h5)</li> <li>the MR data reconstructed with SIRF that has been converted to NIfTI format (SIRF_recon.nii)</li> <li>the script used to generate the data (SIRF_recon.sh)</li> </ul>
Dataset: Effective T-matrix of a cylinder filled with a random 2D particulate
<p>This data is the one used in the paper "Effective T-matrix of a cylinder filled with a random 2D particulate", currently submitted to the Proceedings A of the Royal Society. A preprint version of this paper can be found at: https://arxiv.org/abs/2308.13338</p> <p>This dataset contains the numerically computed effective T-matrix of a cylinder filled with a random 2D particulate. Since the T-matrix is diagonal, only the diagonal elements T_n are computed. Values of T_n are provided for various set of parameters (frequency, particle type and volume fraction). Furthermore, for each set of parameters, T_n is computed with three different methods:</p> <p>1) The Monte Carlo method (MC), which requires computing the scattered field for several configurations of particles.</p> <p>2) The Effective Waves Method (EWM), based on results on random particulate materials. It provides a formula of the effective T-matrix with an effective wavenumber.</p> <p>3) A simplified version of the EWM when only monopole scattering is accounted for (EWM-MA). </p> <p>This dataset includes the following files: metadata_MC.csv and per each parameter one csv file with data records. The notations used in the headers of the files MCx.csv are described in the file header_notations.pdf.</p>
Materials for 2d representation of the HathiTrust Library
<p>Materials to create the LargeVis visualization online at http://creatingdata.us/datasets/hathi-features/, and described in <em>Benjamin Schmidt, "Stable random projection: lightweight, general-purpose dimensionality reduction for digitized libraries," Journal of Cultural Analytics. October 3, 2018.</em></p> <p>Two items. First, `hathi_pca.bin`: a binary file with 100-dimensional representations of the complete Hathi Trust Extended Features set. These began as 1280-dimensional SRP features, and were reduced to 100 dimensions using a PCA transformation matrix derived using a random sample of the full 13 million book set. Vectors were reduced to unit length before PCA, but not afterwords; this means that in general, their length gives some sense of much information was lost in the PCA representation. This can be read using the code at https://github.com/bmschmidt/pySRP, or anything that reads word2vec formatted vectors. Includes HathiTrust identifiers.</p> <p>Second, `hathi.tsv.gz`: a row oriented set containing a variety of metadata fields for each set, including (as 'x' and 'y') the coordinates of a 2-d LargeVis visualization. This is the immediate input to the visualization at ttp://creatingdata.us/datasets/hathi-features/. Columns should be relatively straightforward; they are derived from the HathiTrust MARC records, which can be accessed through Hathi's public API. Classification codes ('lc1') are using the Library of Congress classification; they represent the subclass (generally two characters, though it can be one or three). The first character alone represents the LC class and can be useful for coloring high-level overviews.</p> <p>These two files can be merged through the Hathi Trust identifier present in both.</p> <p> </p>
Beak Shape in Birds and Squid: Principal Components Analysis of 2D Landmarks
<p>R code to analyze observations of beak traces from specimens of birds and squid.</p> <p>Notes are in the code. Watch for updates.</p> <p>Where the csv files include data published by different authors, the doi references to the original publications are included in the R code. I took care to correctly download/process/transcribe where applicable, but please do notify me if there are errors.</p>
Coordinates tracing 2D outlines of beaks (birds and squid)
<p>Two-dimensional coordinates for lines traced onto images of beaks.</p> <ul> <li>This is a .zip archive xy coordinates (250 files, .txt); and a list of specimen names (1 file, .csv).</li> <li>All images traced in FIJI.</li> <li>For each specimen, there is a trace of the beak rostrum and a separate trace of the beak bite surface.</li> <li>Each trace file should be a list of xy coordinates that ends at the beak tip. This must be checked/verified/corrected for all files before running any analyses! I recommend visual inspection by plotting each beak dataset as a scatterplot in a color spectrum (rainbow, etc.).</li> <li>These were traced over pixel images, so each file has a different number of xy coordinates (depending on the pixel resolution/image size that was traced).</li> <li>All bird specimen images were downloaded from Phenome10k.org</li> <li>All cephalopod specimens were traced from images published in: <ul> <li>Xavier, J. C. & Cherel, Y. 2009 Cephalopod beak guide for the Southern Ocean. British Antarctic Survey.</li> </ul> </li> </ul>
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
New insights into the generalized Rutherford equation for nonlinear neoclassical tearing mode growth from 2D reduced MHD simulations
<p>Two dimensional reduced MHD simulations of neoclassical tearing mode growth and suppression by ECCD are performed. The perturbation of the bootstrap current density and the EC drive current density perturbation are assumed to be functions of the perturbed flux surfaces. In the case of ECCD, this implies that the applied power is flux surface averaged to obtain the EC driven current density distribution. The results are consistent with predictions from the generalized Rutherford equation using common expressions for $\Delta^\prime_{\rm bs}$ and $\Delta^\prime_{\rm ECCD}$. These expressions are commonly perceived to describe only the effect on the tearing mode growth of the helical component of the respective current perturbation acting through the modification of Ohm's law. Our results show that they describe in addition the effect of the poloidally averaged current density perturbation which acts through modification of the tearing mode stability index. Except for modulated ECCD, the largest contribution to the mode growth comes from this poloidally averaged current density perturbation.</p>
Auxiliary files and data to generate eddy flux and validate 2D model for MALTA
<p>This repository contains the following directories to accompany the manuscript 'A Zonally-Averaged Global Atmospheric Transport Model for Long-lived Trace Gases', submitted to JAMES:</p><p>1) <strong>GEOSChem </strong>This directory contains the run directory template and (slurm) runscript to generate the tracer fields used to generate the eddy fluxes. The GEOSChem model will have to be installed locally to run this, and the run directory built to your local area. It may be easiest to just copy the relevant bits in /Tracer_2D_template/ (i.e., the .rc files, /RestartFiles/, input.geos, reset_restart.py and species_database.yml) into a GEOSChem Transport run directory and change the directories in the copied files. If using slurm on an HPC, just change the directories in the runtracers_inputs.sh script to match that of your own HPC. Else, a different script will have to be written copying the slurm functionality.</p><p>2) <strong>GEOSChem_SF6 </strong>This directory contains the monthly mean SF6 mole fractions generated using GEOSChem used to validate the 2D model MALTA. Emissions come from the EDGAR v4.2 emissions inventory. Emissions after 2008 continue to use 2008 as the emissions value.</p><p>3) <strong>CFC11_inversion</strong> This directory contains the relevant script and files to quantify emissions of CFC-11 using an output mole fraction from the TOMCAT 3D model using MALTA, and compare these to the TOMCAT emissions used to generate the mole fractions. The directory paths at the beginning of the main script in CFC11_inversion.py must be changed to point to the remaining files in the /CFC11_inversion/ directory, and a save directory must be specified, before running locally. MALTA must be installed to run this.</p><p>4) <strong>singapore.dat </strong>This file contains the QBO winds above Singapore, taken from https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html</p><p> </p>
Feasibility of 3D Body Tracking from Monocular 2D Video Feeds in Musculoskeletal Telerehabilitation
<p>Musculoskeletal conditions affect millions of people globally, however, conventional treatments pose challenges concerning price, accessibility, and convenience. Many telerehabilitation solutions offer an engaging alternative but rely on complex hardware for body tracking. This work explores the feasibility of models for 3D Human Pose Estimation (HPE) from monocular 2D videos (MediaPipe Pose) in a physiotherapy context, by comparing its performance to ground truth measurements. MediaPipe Pose was investigated in eight exercises typically performed in musculoskeletal physiotherapy sessions, where the Range of Motion (ROM) of the human joints was the evaluated parameter. This model showed the best performance for shoulder abduction, shoulder press, elbow flexion, and squat exercises (MAPE ranging between 14.9% and 25.0%, Pearson’s coefficient ranging between 0.963 and 0.996, and cosine similarity ranging between 0.987 and 0.999). Some exercises (e.g. seated knee extension and shoulder flexion) posed challenges due to unusual poses, occlusions and depth ambiguities, possibly related to a lack of training data. This study demonstrates the potential of HPE from monocular 2D videos, as a markerless, affordable and accessible solution for musculoskeletal telerehabilitation approaches. Future work should focus on exploring variations of the 3D HPE models trained on physiotherapy-related datasets, such as the Fit3D dataset, and post-preprocessing techniques to enhance the model's performance.</p>
[MedMNIST+] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification with Multiple Size Options: 28 (MNIST-Like), 64, 128, and 224
<h2><strong>Code</strong> [<a href="https://github.com/MedMNIST/MedMNIST" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1038/s41597-022-01721-8" target="_blank" rel="noopener">Nature Scientific Data'23</a> / <a href="https://doi.org/10.1109/ISBI48211.2021.9434062" target="_blank" rel="noopener">ISBI'21</a>] | <strong>Preprint</strong> [<a href="https://arxiv.org/abs/2110.14795" target="_blank" rel="noopener">arXiv</a>]</h2> <p> </p> <p><strong>Abstract</strong></p> <p>We introduce MedMNIST, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708K 2D images and 10K 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at <a href="https://medmnist.com/">https://medmnist.com/</a>.</p> <p><em><strong>Disclaimer</strong></em>: The only official distribution link for the MedMNIST dataset is <a href="https://doi.org/10.5281/zenodo.10519652">Zenodo</a>. We kindly request users to refer to this original dataset link for accurate and up-to-date data.</p> <p><strong><em>Update</em>:</strong> We are thrilled to release <a href="https://github.com/MedMNIST/MedMNIST/blob/main/on_medmnist_plus.md">MedMNIST+</a> with larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D. As a complement to the previous 28-size MedMNIST, the large-size version could serve as a standardized benchmark for medical foundation models. Install the latest API to try it out!</p> <p> </p> <p><strong>Python Usage</strong></p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>% pip install medmnist<br>% python</pre> <div> <div>To use the standard 28-size (MNIST-like) version utilizing the downloaded files:</div> <br> <div>>>> from medmnist import PathMNIST</div> <div>>>> train_dataset = PathMNIST(split="train")</div> <br> <div>To enable automatic downloading by setting `download=True`:</div> <br> <div>>>> from medmnist import NoduleMNIST3D</div> <div>>>> val_dataset = NoduleMNIST3D(split="val", download=True)</div> <br> <div>Alternatively, you can access MedMNIST+ with larger image sizes by specifying the `size` parameter:</div> <br> <div>>>> from medmnist import ChestMNIST</div> <div>>>> test_dataset = ChestMNIST(split="test", download=True, size=224)</div> </div> </blockquote> <p> </p> <p><strong>Citation</strong></p> <p>If you find this project useful, please cite both v1 and v2 paper as:</p> <blockquote> <p>Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.</p> <p>Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis". IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.</p> </blockquote> <p>or using bibtex:</p> <blockquote> <pre>@article{medmnistv2, title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification}, author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing}, journal={Scientific Data}, volume={10}, number={1}, pages={41}, year={2023}, publisher={Nature Publishing Group UK London} } @inproceedings{medmnistv1, title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis}, author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing}, booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, pages={191--195}, year={2021} }</pre> </blockquote> <p>Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the <a href="https://medmnist.github.io/">project website</a>.</p> <p> </p> <p><strong>License</strong></p> <p>The MedMNIST dataset is licensed under <em>Creative Commons Attribution 4.0 International</em> (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>), except DermaMNIST under <em>Creative Commons Attribution-NonCommercial 4.0 International</em> (<a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a>).</p> <p>The code is under <a href="https://github.com/MedMNIST/MedMNIST/blob/main/LICENSE">Apache-2.0 License</a>.</p> <p> </p> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.10519652">v3.0</a> (this repository): Released MedMNIST+ featuring larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D.</p> <p><a href="https://doi.org/10.5281/zenodo.10519195">v2.2</a>: Removed a small number of mistakenly included blank samples in OrganAMNIST, OrganCMNIST, OrganSMNIST, OrganMNIST3D, and VesselMNIST3D. </p> <p><a href="https://doi.org/10.5281/zenodo.6496656">v2.1</a>: Addressed an issue in the NoduleMNIST3D file (i.e., nodulemnist3d.npz). Further details can be found in this <a href="https://github.com/MedMNIST/MedMNIST/issues/22#issuecomment-1103438191">issue</a>.</p> <p><a href="https://doi.org/10.5281/zenodo.5208230">v2.0</a>: Launched the initial repository of MedMNIST v2, adding 6 datasets for 3D and 2 for 2D.</p> <p><a href="https://doi.org/10.5281/zenodo.4269852">v1.0</a>: Established the initial repository (in a separate repository) of MedMNIST v1, featuring 10 datasets for 2D.</p> <p> </p> <p><strong>Note</strong>: This dataset is <strong>NOT</strong> intended for clinical use.</p>
ECG 2D
<p>The original version of this dataset was formatted by R. Olszewski as part of his thesis <em>Generalized feature extraction for structural pattern recognition in time-series data</em> at Carnegie Mellon University, 2001. Each series traces the electrical activity recorded during one heartbeat. The two classes are a normal heartbeat and a Myocardial Infarction.</p> <p>This is a modified multivariate version of the dataset saved in numpy format. The original dataset was obtained from <a href="https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/">here</a>.</p> <p>The data are 3-dimensional arrays of shape [n_samples, time_steps, n_variables]. The data can be loaded as follows:</p> <pre><code>loaded_data = np.load("ECG_2D.npz") Xtr = loaded_data['Xtr'] # Training data of shape (100, 152, 2) Ytr = loaded_data['Ytr'] # Training labels of shape (100, 1) Xte = loaded_data['Xte'] # Test data of shape (100, 152, 2) Yte = loaded_data['Yte'] # Test labels of shape (100, 1)</code></pre>
Epitaxial HfTe2 Dirac semimetal in the 2D limit (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Epitaxial HfTe<sub>2</sub> Dirac semimetal in the 2D limit</em>" by P. Tsipas et al., APL Materials <strong>9</strong>, 101103 (2021); <a href="https://doi.org/10.1063/5.0065839">https://doi.org/10.1063/5.0065839</a></p> <p>An Open Access version of the paper can be found here: <a href="https://doi.org/10.1063/5.0065839">https://doi.org/10.1063/5.0065839</a></p>
Ultrathin epitaxial Bi film growth on 2D HfTe2 template (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Ultrathin epitaxial Bi film growth on 2D HfTe<sub>2</sub> template</em>" by E. Xenogiannopoulou et al., 2022 <em>Nanotechnology</em> <strong>33</strong> 015701; <a href="https://doi.org/10.1063/5.0038799">https://doi.org/10.1088/1361-6528/ac2d08</a></p> <p>An Open Access version of the paper can be found here: <a href="https://zenodo.org/record/4562057#.YaDC4NBBxPY">https://zenodo.org/record/5720132#.YaDKWNBBxPb</a></p>
Dataset for "CVD growth of self-assembled 2D and 1D WS2 nanomaterials for the ultrasensitive detection of NO2"
<p>This file contains the raw data used in the paper entitled CVD growth of self-assembled 2D and 1D WS2 nanomaterials for the ultrasensitive detection of NO2 published in Sensors and Actuators: B. Chemical 326 (2021) 128813</p> <p>DOI: <a href="https://doi.org/10.1016/j.snb.2020.128813">10.1016/j.snb.2020.128813</a></p>
New dataset obtained from 2D positioning system with distributed control
<p>The dataset contains the obtained trajectory (encoder measurements) of the 2D positioning system, as well as the reference (commanded) trajectory. The control task is distributed to the low-level controllers for x and y axes synchronized using IEEE 1588 Precision Time Protocol (PTP), where the movement of the axes is realized based on the data that the low-level controllers receive from the high-level controller. The dataset includes 60 signals (30 measurements for x and y axis) that represent obtained trajectories and 2 signals (x and y axes) that represent commanded trajectory, where the length of each signal is 61,000 samples. The system was designed and built by the Cyber-Physical Systems Lab at the Pratt School of Engineering, Duke University, where it is located. Table 1 shows the list of collected signals, whereas a detailed description of the system can be found in [1]. For more information, see [2, 3].</p>
Experimental testing of 2D Optical Phased Array (OPA) performance
<p>Radiation pattern of single element antenna in PolyBoard platform (with 0.1- and 0.5-degrees resolution)</p>
2D synthetic hydrothermal models
<p>2D hydrothermal models of 1 km × 1 km in the horizontal and depth directions, with a grid size of 50 × 50, created by TOUGH2 software. The 2D models were used to evaluate physics-informed neural networks in the following paper:</p><p>Ishitsuka, K. and Lin, W. (2023) Physics-informed neural network for inverse modeling of natural-state geothermal systems, Applied Energy, 337, 120855. <a href="https://doi.org/10.1016/j.apenergy.2023.120855">https://doi.org/10.1016/j.apenergy.2023.120855</a></p><p>https://www.sciencedirect.com/science/article/pii/S0306261923002192</p><p>In the files, "x" and "z" indicate horizontal and vertical coordinates in meter. The columns of "pres", "temp" and "log10PER" indicate pressure, temperature and logarithm of permeability, respectively. </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.