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994 results for “2d”
How does a Moka Pot work? 2D X-Ray video gives insights!
<p>This sequence of X-Ray images shows how one of the most common Italian moka pots actually work! The sequence starts with a completely prepared moka pot (water in the bottom part, coffee in the middle and hot plate on). During the process the water starts to boil and the steam pressure pushes the hot water through the coffee into the bassin at the top of the pot.</p> <p>This video sequence and additional explanations can also be found on Wikipedia under:</p> <ul> <li><a href="https://en.wikipedia.org/wiki/Moka_pot">Wikipedia Moka pot english</a></li> <li><a href="https://de.wikipedia.org/wiki/Espressokanne">Wikipedia Moka pot german</a></li> </ul> <p> </p> <p>The data set contains:</p> <ul> <li>TIF-stack of the raw footage (sequence of X-Ray images)</li> <li>3 artificially colored images in the beginning (Bottom), mid (Mid) and end (Top) of the process.</li> </ul> <p>The colored images are based on an image processing workflow which includes the time-derivative of the raw seqeunce, minima and maxima projections, HUE color-space transformation, etc.</p>
Oscillatory compression with different frequencies of 2D, dense, soft particle suspensions
<p>This repository contains 5 datasets of cyclically compressed hydrogel packings inside microfluidic channels, with different oscillation frequencies, observed using a microscope. This repository contains the raw data (images) as well as analyzed data of the particles tracked over time. The data format closely resembles information you might obtain from 2D DEM simulations, and could, therefore, be used to calibrate DEM simulations of the compaction of soft particles.</p> <p>The "Readme.md" file contains more in-depth information about the experimental setup, experiments and data structure.</p>
Digital Repository of Ireland Member Digitisation Workflows for 2D Image Files: Survey Questions and Dataset
<p>The Digital Repository of Ireland (DRI) issued a survey to its membership, <strong>DRI Member Digitisation Workflows for 2D Images</strong>, which ran from December 7, 2023–January 31, 2024. The survey was conducted to improve the DRI’s understanding of the technical processes and metadata workflows that our members use to digitise and share images in the Repository, in order to better tailor our support for this work and deliver the most complete information about digital images files available to our users. </p> <p>The survey informed the actions taken in WorldFAIR Project WP13 deliverable <a href="https://doi.org/10.5281/zenodo.10850009" target="_blank" rel="noopener">13.3 Implementing and Testing the Cultural Heritage Image Sharing Recommendations: DRI Case Study Report</a>. The data will inform ongoing work at DRI aimed at improving the transparency of technical information associated with digital assets accessed through the Repository.</p> <p>Read more about the Cultural Heritage Image Sharing Case Study DRI on our website: <a href="https://dri.ie/the-worldfair-project/">https://dri.ie/the-worldfair-project/</a>. </p> <p>Summary: DRI is Ireland's national repository for the arts, humanities, and social sciences data, and operates on a membership scheme. There were 20 respondents to the survey, giving us a response rate of about 35% of DRI's membership. Representation from professional fields of work across the cultural heritage sector was captured in the results (note that some institutions gave multiple responses): 17 Archives, 12 Libraries, 5 Museums and 11 Higher Education Institutions. </p>
Deciphering hot- and multi-exciton dynamics in core–shell QDs by 2D electronic spectroscopies
<p>2D spectroscopy datasets from PCCP 20 (2018) 18176, DOI: 10.1039/c8cp02574f</p> <p>Dasets are in the Matlab format .mat, each one containing:</p> <p>R(or N or T).X = 3-dimensional matrix containing 3d signal. dimensions=(w1,w3,t2)<br> R.t= t2 axis<br> R.f= w1=w3 axis</p> <p> </p> <p>R=rephasing; N=non-rephasing; T=total signal</p> <p>2D-BC=2D photon echo in BOXCARS configuration; 2D-PP= 2D pump-probe in quasi-collinear configuration.</p>
In silico 2D photoacoustic imaging data
<p>Here you find the data that was used for the experiments in the paper <strong>Confidence estimation for machine learning-based quantitative photoacoustics</strong> by <em>Janek Gröhl</em>, <em>Thomas Kirchner</em>, <em>Tim Adler</em>, and <em>Lena Maier-Hei</em>n.</p>
Replication Data for: Probing magnetism in 2D materials at the nanoscale with single spin microscopy
<p>Data repository for: <strong>Probing magnetism in 2D materials at the nanoscale with single spin microscopy</strong></p> <p><em>Data description.pdf </em>describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>MzFromBNV.m</em>, <em>kvalues.m</em>, <em>NVZeemanShiftFromMagnetizedSampleEdge.m</em> are Matlab code files used to transform and fit the data.</p> <p> </p>
3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images
<p>3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT).</p> <p>It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers.</p> <p>The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known.</p> <p>All the images are of blocky 3D shapes floating in space in front of a black background.</p> <p>Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a></p> <p>The dataset consists of:</p> <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> <strong>(Training Dataset)</strong> <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> <strong>(Test Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1].<br> <strong>Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</strong></li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> <strong>(Validation Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul> <p> </p> <p><strong>Important:</strong> Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "<strong>unxz</strong><strong> 3diqtt-v2-train.h5.xz</strong>" on Unix machines.</p> <p><strong>If you use this dataset, please cite it.</strong></p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: 2D segmentations
<p>This dataset contains 2D segmentations of images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation algorithm, 3D visualization and geometric analysis are presented in the corresponding manuscript. Files are uploaded in .jpg format and are named: lamella_slicenumber, with slicenumber varying from 1 to 8100. There is also a Matlab file, UndulationData_Zenodo.mat, in which all the relevant variables post analysis are stored. This file contains a variable called "myFiles", which contains the link between the slicenumbers used here and the original dataset that is published in Zenodo (.tif synchrotron images).</p>
Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework
<p>[This repository contains the source data for the manuscript "<strong>Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework</strong>" https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/47951-a-real-time-optical-and-electronic-chemical-sensor-based-on-a-amino-enone-linked-triazine-containing-2d-covalent-organic-framework]</p> <p>Fully-aromatic, two-dimensional covalent organic frameworks (2D COFs) are hailed as candidates for electronic and optical devices, yet to-date few applications emerged that make genuine use of their rational, predictive design principles and permanent pore structure. Here, we present a 2D COF made up of chemoresistant β-amino enone bridges and Lewis-basic triazine moieties that exhibits a dramatic real-time response in the visible spectrum and an increase in bulk conductivity by two orders of magnitude to a chemical trigger - corrosive HCl vapours. The optical and electronic response is fully reversible using a chemical switch (NH<sub>3</sub> vapours) or physical triggers (temperature or vacuum). These findings demonstrate a useful application of fully-aromatic 2D COFs as real-time responsive chemosensors and switches.</p>
Dataset of "Photodetector Based on the Non-Centrosymmetric 2D Pseudo-Binary Chalcogenide MnIn2Se4"
<p>Due to their attractive band gap properties and Van de Waals structure 2D binary chalcogenides materials have been widely investigated in the last decade, finding applications in several fields such as catalysis, spintronic, and optoelectronic. Ternary 2D chalcogenide materials are a subject of growing interest in material science due to their superior chemical tunability which endows tailored properties to the devices prepared thereof. In the family of AIIBIII2XVI4, ordered ZnIn2S4-like based photocatalytic systems have been studied meticulously. In contrast, reports on disordered phases appear to a minor extent. Herein, a photo-electrochemical (PEC) detector based on the pseudo-binary MnIn2Se4 system is presented. A combination of optical measurements and DFT calculations confirmed that the nature of the bandgap in MnIn2Se4 is indirect. Its performances outclass parent compounds, reaching responsivity values in the order of 8.41 mA W-1. The role of the non-centrosymmetric crystal structure is briefly discussed as a possible cause of the improved charge separation of the photogenerated charge carriers. </p>
2D NMR HSQC spectra of proteins and mouse urine with peaks picked by DEEP Picker
<p>2D 15N-1H HSQC NMR spectra of Im7 and a-synuclein with peak lists produced by DEEP Picker.</p> <p>2D 13C-1H HSQC NMR spectrum of mouse urine with peak lists determined by DEEP Picker.</p>
2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware
<p>This dataset contains the raw data used for the publication:</p> <p><strong>2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware</strong></p> <p>By Lei Tong<sup>1</sup>, Zhuiri Peng<sup>1</sup>, Runfeng Lin<sup>1</sup>, Zheng Li<sup>1</sup>, Yilun Wang<sup>1</sup>, Xinyu Huang<sup>1</sup>, Kan-Hao Xue<sup>1</sup>, Hangyu Xu<sup>2</sup>, Feng Liu<sup>3</sup>, Hui Xia<sup>2</sup>, Peng Wang<sup>2</sup>, Mingsheng Xu<sup>4</sup>, Wei Xiong<sup>1</sup>, Weida Hu<sup>2,</sup>*, Jianbin Xu<sup>5</sup>, Xinliang Zhang<sup>1</sup>, Lei Ye<sup>1,</sup>*, Xiangshui Miao<sup>1</sup></p> <p>Detailed descriptions for each file can be found in "Dataset description.docx".</p>
Nuclear Magnetic resonance Dataset of 2D spectra of S100B and Tau to study their protein-protein interaction
<p>Nuclear Magnetic resonance dataset of 2D spectra corresponding to raw data of research published in Nature Communication in a communication entitled "Dynamic interactions and Ca2+ 1 -binding modulate the holdase-type chaperone activity of S100B preventing tau aggregation and seeding" by Moreira G. et al.</p> <p>Dataset corresponds to</p> <p>raw data files in Bruker format of NMR 2D spectra (ser), associated with files of acquisition parameters and processing parameters (pdata),</p> <p>files in .ucsf format that can be read with NMRFAM-Sparky (free download) of 2D spectra (in sub-directory pdata/1)</p> <p>files of chemical shift value lists that can be read as text files or in NMRFAM sparky together with the corresponding ucsf files.</p> <p>physico-chemical conditions are found in title in pdata\1</p> <p>Data were acquired on a Bruker 900-MHz spectrometer equipped with a triple-resonance cryogenic probe (Bruker, Karlsruhe, Germany)</p>
LiftWEC deliverable D4.3: Dataset from 2D experimental test campaign
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. "LiftWEC Deliverable D4.3 Report on 2D experimental testing dataset" describes this dataset and for a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
2D Cardiac black-blood TSE MR raw data
<p>Raw data in ismrmrd format obtained with a 2D black-blood TSE sequence on a 3T Siemens Verio scanner in three different orientations. This data is used as test data for the comparison of different open-source image reconstruction packages provided here: <a href="https://github.com/ckolbPTB/OpenSourceMrRecon">OpenSourceMrRecon</a></p>
2D LSFM timelapse of cardiomyocyte calcium dynamics
<p>Uploaded zip-folder contains the following files:<br> 1. A representative raw dataset of a 2D LSFM ventricular cardiomyocyte undergoing stimulated calcium transients and calcium sparks (frame_0000.tif -frame_17999.tif)<br> 2. The recorded pacing signal time trace (waveform_test.xslx)<br> 3. Image corresponding to the time-averaged background (AVG_19_35_39_LowNA rolling shutter.tif)<br> 4. Pre-processed nuclear mask matrix (NuclearMask.mat), CMO-channel average (CMO_Average.mat), and CMO channel maximum intensity projection (CMO_MIP). <br> 5. Split and co-registered data for each spectral channel (CMO_frame_00001.tif-CMO_frame_18000.tif, FLUO4_frame_00001.tif -FLUO4_frame_18000.tif).<br> <br> Compressed file size: 14.9 GB<br> Uncompressed file size: 42.8 GB. <br> <br> Related to the following manuscript: <br> Liuba Dvinskikh, Hugh Sparks, Ken MacLeod and Chris Dunsby " <em>High-speed 2D light-sheet fluorescence microscopy enables quantification of spatially varying calcium dynamics in ventricular cardiomyocytes</em>" (2023), <em>In review</em> with Frontiers in Physiology, Cardiac Electrophysiology. </p>
Datasets supplementing journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy" in Nanoscale 2022
<p>Datasets supporting the Nanoscale journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy".</p> <p>NAP-XPS.zip: Near-ambient pressure X-ray photoelectron spectroscopy, Figures 2, 3. (NEP 101007417)</p> <p>STM.zip: Scanning tunneling microscopy, Figure 5a, inset. (NEP 101007417)</p> <p>TPD.zip: Temperature programmed desorption, Figure 1a.</p> <p>UHV-XPS.zip: X-ray photoelectron spectroscopy, Figure 1b,c.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007417, having benefited from the access provided by by ALBA in Barcelona (Spain) and CNR-IOM in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID075.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Hyperspectral 2D fan-beam X-ray CT dataset of 5 materials
<p>Hyperspectral X-ray CT dataset acquired at the DTU 3D imaging center. The phantom consists of 5 materials: Aluminium (10 mm) and PVC (7.8 mm) in solid blocks. Sugar, H2O2, and H2O in circular glass containers.</p> <p>3D array with dimension: 128 x 370 x 258 < channel, angle, horizontal ></p> <p> </p> <p>Detector parameters:</p> <p>Number of detector pixels: 258 (concatenated from 2 detector modules with 128 pixels each and 2 pixel interpolated across a gap between detectors)</p> <p>Pixel size: 0.077 cm</p> <p>Sep=0.153 Pixels' gap length (cm)</p> <p>det_space=(ndet)*pixel_size+Sep # physical width of detector in cm (pixels*pixel_size), including the gap</p> <p> </p> <p>Acquisition Parameters</p> <p>360 # Angular span of projections in degrees</p> <p>370 # Number of projections. note: last projection taken is not a duplicate of the first projection. At angle: 360/370 degrees from first projection.</p> <p>115.0 # Source-Detector distance in cm</p> <p>0 # Vertical source shift from perfect placement</p> <p>0 # Vertical detector shift from perfect placement</p> <p>57.5 # Source-AxisOfRotation distance in cm</p> <p> </p> <p>rot_axis_x = 0 # x-position offset of AxisOfRotation</p> <p>rot_axis_y = 0 # y-position offset of AxisOfRotation</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.