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3,186 results for “Efficiency”
Gate tunability of highly efficient spin-to-charge conversion by spin Hall effect in graphene proximitized with WSe2
<p>Data associated with "Gate tunability of highly efficient spin-to-charge conversion by spin Hall effect in graphene proximitized with WSe<sub>2</sub>" </p> <p>Publication: <a href="https://arxiv.org/abs/2006.09227">https://arxiv.org/abs/2006.09227</a> and <a href="https://aip.scitation.org/doi/10.1063/5.0006101">https://aip.scitation.org/doi/10.1063/5.0006101</a></p> <p><br> </p>
An inflamed human alveolar model for testing the efficiency of anti-inflammatory drugs in vitro
<p>The data set accompanies the study where we developed an inflamed human alveolar epithelium model and to test the resolution<strong><em> </em></strong>lipopolysaccharide (LPS)-induced inflammation <em>in vitro</em> with a corticosteroid, methylprednisolone (MP). A specific focus of the study was in macrophage phenotype shifts in response to these stimuli.</p> <p>The data set includes:</p> <p>- Pro-inflammatory marker (interleukin (IL)-8, tumor necrosis factor α (TNFα), IL1β) secretion data, analysed via ELISA, and cell viability (analysed via lactate dehydrogenase assay) of both monocultures (human monocyte-derived macrophages) and of the multicellular human alveolar model, composed of macrophages, dendritic cells, and epithelial cells. </p> <p>- Barrier permeability data of the multicellular model, assessed via labeled-dextran permeability assay. </p> <p>All the above-stated data is joined in the file: DraslerB_Frontiers 2020_Inflammatory model. Sample codes are explained int he first tabs. </p> <p>- Pro-inflammatory marker gene expression data of the multicellular model, assessed via real time RT-qPCR. The data prepared for analysis and analysed is joined to the above-mentioned folder, whereas the direct PCR runs are joined in the file: DraslerB_Frontiers 2020_Inflammatory model_PCR runs. </p> <p>- Confocal laser scanning microscopy raw files (lsm) of the multicellular model can be opened with an open source software Fiji, based on ImageJ. </p>
Data for "How Nitrogen and Phosphorus Availability Change Water Use Efficiency in a Mediterranean Savanna Ecosystem"
<p>These are flux and meteorological data for the measurement sites ES-LMa (CT; control treatment), ES-LM1 (NT; nitrogen treatment), and ES-LM2 (nitrogen + phosphorus treatment) for the period from 2014-03-20 to 2020-02-01.</p> <p>These data were used for the manuscript:</p> <p>El-Madany, et al. (2021) "How Nitrogen and Phosphorus Availability Change Water Use Efficiency in a Mediterranean Savanna Ecosystem" submitted to Journal of Geophysical Research - Biogeoscience.</p>
Supplemental artifacts of the paper: Efficient Binary-Level Coverage Analysis
<p>NOTE: the official repository of bcov is: <a href="https://github.com/abenkhadra/bcov">https://github.com/abenkhadra/bcov</a></p> <p>This repository contains the artifacts accompanying our paper: "Efficient Binary-Level Coverage Analysis", which appeared in ESEC/FSE'20. The artifacts consists of two packages, namely, bcov-benchmarks.tar.gz and bcov-artifacts.tar.gz. The former package contains the complete list of binaries described in our experiments. The artifacts of the latter package are organized as follows:</p> <p> - <strong>sample-binaries</strong>. Folder that contains sample binaries patched with bcov.</p> <p> - <strong>dataset.tar.gz</strong>. Package containing experimental data in csv format.</p> <p> - <strong>figures</strong>. Folder that contains the python script used to generate the figures<br> of our paper. It assumes that the dataset was first extracted to the folder `dataset`.</p> <p> - <strong>install.sh</strong>. This script builds and installs bcov together with its dependencies.</p> <p> - <strong>experiment-01.sh</strong>. This script patches our sample binaries and shows how coverage<br> data can be collected. It assumes that bcov was installed using the previous script.</p> <p> - <strong>bcov.tar.gz</strong>. Source code of the first public version of `bcov`. The tool is distributed under an MIT license.<br> </p>
Efficient PCA denoising of spatially correlated redundant MRI data
<p>MRI data used for the study: "Henriques, Ianus, Novello, Jovicich, Jespersen, Shemesh. Efficient PCA denoising of spatially correlated redundant MRI data. Imaging Neuroscience (In Press)."</p><p><strong>Preclinical scanner data</strong></p><p>All animal experiments for the collection of these datasets were preapproved by the institutional and national authorities and carried out according to European Directive 2010/63.</p><p>A mouse brain (C57BL/6J) was extracted via transcardial perfusion with 4% Paraformaldehyde (PFA), immersed in 4% PFA solution for 24 h, washed in Phosphate-Buffered Saline (PBS) solution for at least 24 h, and then placed on a 10 mm NMR tube filled with Flourinert (Sigma Aldrich, Lisbon, PT), which was sealed using paraffin film. </p><p>The MRI experiments were performed on a 16.4 T Bruker Aeon Ascend scanner (Bruker, Karlsruhe, Germany), interfaced with an Avance IIIHD console, and equipped with a gradient system capable of producing up to 3000 mT/m in all directions. A constant temperature of 37oC was maintained throughout the experiments using the probe's variable temperature capability. </p><p>Two distinct diffusion-weighted datasets were then acquired using Bruker's standard "Diffusion Tensor Imaging EPI":</p><ul><li><i>Dataset1 </i>(<strong>MB_exp1.nii</strong> and its brain mask<strong> MB_exp1_mask.nii</strong>): For this dataset, we modulated the amount of spatial correlations by acquiring EPI datasets with parameters optimized to mitigate noise spatial correlations, particularly avoiding k-space undersampling acquisition during EPI's gradient ramps and without using partial Fourier, which minimize regridding.</li><li><i>Dataset2 </i>(<strong>MB_exp2.nii</strong> and its brain mask<strong> MB_exp2_mask.nii</strong>): The second dataset was acquired with identical resolution, number of acquisitions, etc., but with large factors inducing spatial correlations, including k-space sampling during gradient ramps (default Bruker's acquisition and reconstruction procedures for acquisition speed) and with a significant phase partial Fourier factor of 6/8 (note for partial Fourier acquisitions, EPI data is reconstructed with zero-padding, according to the default reconstruction procedures by Bruker's pre-clinical reconstruction software Paravision 6.0.1).</li></ul><p>All datasets are acquired for the following diffusion-weighted parameters: 30 gradient directions for b-values 1, 2 and 3 ms/μm2 (Δ = 15 ms, δ = 1.5 ms), and 20 consecutive b-value=0 acquisitions - b-values and diffusion gradient directions are saved in files: <strong>MB.bval</strong> / <strong>MB.bvec</strong>.</p><p>Other acquisition parameters: TR/TE = 3000/50 ms, 9 coronal slices, Field of View = 12×12 mm2, matrix size 80×80, in-plane voxel resolution of 150×150 μm2, slice thickness = 0.7 mm, number of averages = 2, number of segments = 1, double sampling acquisition.</p><ul><li><i>Gold standard acquisitions for dataset 2 </i>(<strong>MB_exp2_20averages.nii</strong>): For a gold standard reference, the second dataset was also repeated for 20 averages. Note, since this dataset is aligned to <strong>MB_exp2.nii</strong> you can use <strong>MB_exp2_mask.nii </strong>for its brain mask.</li></ul><p>For all datasets, Spatial drifts in the image domain were first corrected using a sub-pixel registration technique (Guizar-Sicairos et al., 2008).</p><p> </p><p><strong>Clinical scanner data</strong></p><p>Experiments were approved by the Ethical Committee of the University of Trento and the participant signed an informed consent. </p><p>MRI data was a acquired for a healthy control (male, 54 years) using a 3T MAGNETOM PRISMA scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 64-channel head-neck RF receive coil. </p><p>Diffusion MRI data was acquired using a monopolar single diffusion encoding EPI PGSE (Feinberg et al., 2010; Moeller et al., 2010; Xu et al., 2013) along 30 diffusion gradient directions for five non-zero b-values = 1, 2, 3, 4.5 and 6 ms/μm2 (Δ = 39.1 ms, δ = 26.3 ms) and 17 interspersed b-value=0 acquisitions. b-values and diffusion gradient directions are saved in files: <strong>HB.bval</strong> / <strong>HB.bvec</strong>. Note, only the masked version of these dataset (<strong>HB_masked.nii</strong> and its brain mask <strong>HB_mask.nii</strong>) is provided to guarantee that data privacy standards are met. For noise maps covering all FOV, the noise maps computed as the std of the 5 first repeating unmasked b = 0 acquisitions are provided in file <strong>stdS0i.nii.</strong></p><p>Other acquisition parameters were the following: TR/TE = 4000/80 ms, 63 axial slices, Field of View = 220×220 mm2, matrix size 110×110, isotropic resolution of 2 mm, 6/8 phase partial Fourier, parallel imaging with GRAPPA 2, simultaneous multi-slice factor 3. All diffusion MRI data was reconstructed using zero-padding, which is the default procedure for data acquired with partial Fourier above 70%. </p>
Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023).
<p>Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023), DOI 10.1038/s41467-023-41718-4, include images, data used for generate that images, input files, converged potential files used for the calculations on SPR-KKR package 8.6. and raw data files.</p>
An efficient not-only-linear correlation coefficient based on clustering: Supplementary Files
<p>Supplementary Files for the manuscript "An efficient not-only-linear correlation coefficient based on machine learning" available at https://doi.org/10.1101/2022.06.15.496326</p> <ul> <li>Supplementary File 1: All pairwise gene correlations using Pearson, Spearman and CCC among the top 5,000 genes in GTEx’s whole blood with the largest variance. Columns indicates whether the gene pair was categorized in the top or bottom 30% of each coefficient, the correlation value, and the significance of the association. Significance is only present for the top 10 gene pairs of each intersection in the “Disagreements” group (Figure 3a, right) where CCC disagrees with Pearson, Spearman or both.</li> <li>Supplementary File 2: Percentiles of the coefficient values for the top 5,000 genes in GTEx’s whole blood.</li> <li>Supplementary File 3: Pearson, Spearman and CCC correlations values and their significance for two gene pairs (<em>UTY</em> - <em>KDM6A</em> and <em>DDX3Y</em> - <em>KDM6A</em>) across all tissues in GTEx.</li> </ul>
Simulation of the efficiency of a reversed supply chain of wood biomass using different types of transport units (NCN) DEC-2020/39/I/HS4/03533
<p>Data describing simulations related to the standardisation of loading units for the transport of wood biomass. The effectiveness of assumptions relating to the use of different types of packaging were verified from the perspective of the number of vehicles required and their emissions. The relationship between the size and specification of the wood biomass load and the packaging used was indicated. The study was funded by National Science Centre in Poland under agreement National Center of Science (NCN) through grant DEC-2020/39/I/HS4/0353</p>
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks
<p>Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxE<sub>CCT</sub>, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxE<sub>CCT</sub> to SPAGxEmix<sub>CCT</sub>, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> to analyze time-to-event traits in UK Biobank. For the SPAGxE<sub>CCT</sub> analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included. For the SPAGxEmix<sub>CCT</sub> analyses, 338,044 individuals from all ancestries were included. SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.</p>
Arbitrary and active colouring of solar cells with negligible loss of efficiency
<p>This is all the data associated with the journal article. The dataset is organized on a figure-by-figure basis within a compressed ZIP file for ease of access.</p> <ul> <li><strong>Graph Data</strong>: Available in <code>.txt</code> and <code>.xlsx</code> formats, providing raw and processed data used to generate the figures.</li> <li><strong>Images</strong>: All images included in the article are provided in <code>.jpg</code> format.</li> <li><strong>Figure Graphs</strong>: All complete figure graphs are supplied as <code>.pdf</code> files.</li> </ul>
Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"
<p>Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".</p> <p><em><strong>Inputs</strong></em>: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.</p> <ul> <li><em><strong>Real-life</strong></em>: preprocessed event logs (<em>xes</em> and <em>csv</em>) corresponding to the real-life processes used in the evaluation. Process models (<em>pnml</em>) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (<em>txt</em>) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>).</li> <li><em><strong>Synthetic</strong></em>: simulated event logs (<em>csv</em>) corresponding to the synthetic processes used in the evaluation. Designed process models (<em>bpmn</em> and <em>pnml</em>). Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>). Ongoing cases with injected noise as described in the publication (under folders <em>noise_1</em>, <em>noise_2</em>, and <em>noise_3</em>).</li> </ul>
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
Efficient Geometric Algorithms Using Osculating Toroidal Patches
<p>The four objects used for Hausdorff distance computation have been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel ( <a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/ </a>).</p> <p>The algorithm can be applied to any objects, and the specific models that are used for computation in the thesis are given and created by IRIT.</p> <p>Model is provided in OBJ format.</p>
Efficient embryoid-based method to improve generation of optic vesicles from human induced pluripotent stem cells data
<p>Animal models have provided many insights into ocular development and disease, but they remain suboptimal for understanding human oculogenesis. Eye development requires spatiotemporal gene expression patterns and disease phenotypes can differ significantly between humans and animal models, with patient-associated mutations causing embryonic lethality reported in some animal models. The emergence of human induced pluripotent stem cell (hiPSC) technology has provided a new resource for dissecting the complex nature of early eye morphogenesis through the generation of three-dimensional (3D) cellular models. By using patient-specific hiPSCs to generate <em>in vitro </em>optic vesicle-like models, we can enhance the understanding of early developmental eye disorders and provide a pre-clinical platform for disease modelling and therapeutics testing. A major challenge of <em>in vitro </em>optic vesicle generation is the low efficiency of differentiation in 3D cultures. To address this, we adapted a previously published protocol of retinal organoid differentiation to improve embryoid body formation using a microwell plate. Established morphology, upregulated transcript levels of known early eye-field transcription factors and protein expression of standard retinal progenitor markers confirmed the optic vesicle/presumptive optic cup identity of <em>in vitro </em>models between day 20 and 50 of culture. This adapted protocol is relevant to researchers seeking a physiologically relevant model of early human ocular development and disease with a view to replacing animal models.</p>
Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"
<p>Artifact for the paper "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"</p> <p>The package contains:</p> <ul> <li>example files for all static and dynamic fault tree models</li> <li>installation instructions for the three tools</li> <li>scripts to perform the benchmarking</li> <li>detailed result tables</li> </ul>
ASHRAE 1836-RP main list of energy efficiency measures
<p>Energy Efficiency Measures (EEMs) play a central role throughout the building energy efficiency industry, and lists of EEMs therefore exist in a variety of resources. However, each of these use different conventions for describing and organizing measures, which presents a major challenge for aggregating information across these resources. The ASHRAE 1836-RP main list of energy efficiency measures was assembled as part of ASHRAE Research Project 1836 in order to discover trends in how existing resources describe and organize EEMs. Analysis of this dataset supported the overall objective of 1836-RP, which was to develop a standardized system for the categorization and characterization of EEMs.</p> <p>The dataset contains the complete list of 3,490 EEMs assembled and analyzed as part of 1836-RP. The EEMs were collected from 16 different source documents during the 1836-RP literature review from September 2019 through July 2020. An initial list of suggested sources was provided by the members of the 1836-RP Project Advisory Board, and additional documents were added through the authors’ literature review.</p> <p>A data dictionary can be found in the README.txt file. Additional information on working with this dataset can be found in the project repository: <a href="https://github.com/retrofit-lab/ashrae-1836-rp-text-mining">https://github.com/retrofit-lab/ashrae-1836-rp-text-mining</a></p>
Raw data for the journal article "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide"
<p>This data set corresponds to the article by Kong et al. entitled "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide", published in Small Methods</p>
Data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"
<p>This is the experimental data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"</p> <p>It comes in the form of two archives:</p> <ol> <li><strong>merging-artifact.zip</strong>: the whole set of benchmarks with the scripts to run</li> <li><strong>merging-artifact-FORMATS22-results.zip</strong>: all results executed 5 times</li> </ol>
Source code and simulation results for the computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "Computation of eigenfrequency sensitivities using Riesz projections for<br> efficient optimization of nanophotonic resonators" [<a href="https://doi.org/10.1038/s42005-022-00977-1">1</a>]. It combines direct differentiation of scattering problems with a contour integral method [<a href="https://doi.org/10.1016/j.jcp.2020.109678">2</a>] to compute eigenfrequency sensitivities. An optimization is used to demonstrate the relevance of the method.</p> <p><strong>Structure</strong></p> <p>The most important elements of this publication are the MATLAB scripts 'sensitivities.m' and 'optimization.m', which can be used to reproduce the most important results of the paper. The directories <strong>code</strong>, <strong>scattering</strong> and <strong>results </strong>contain the software RPExpand [<a href="https://doi.org/10.1016/j.softx.2021.100763">3</a>], input files for JCMsuite [<a href="https://doi.org/10.1002/pssb.200743192">4</a>] and results produced with the scripts, respectively. Furthermore, the latter contains the subfolder <strong>tabulated,</strong> which contains text files tabulating data presented in Figures 2 and 4 of the paper. Eventually, the function 'code/observation.m' evaluates the target for the optimization.</p> <p><strong>Additional Information</strong></p> <p>The applicaton is based on an example from the literature [<a href="https://doi.org/10.1126/science.aaz3985">5</a>]. Using apriori knowledge about the eigenmode of interest, we chose the scalar observable, as defined in Section B of the paper, to be the component of the electric field normal to the plane defining the solid of revolution.</p> <p>The convergence studies are based on the discrete, circular contour <span>\(\tilde{C} = \big\{ c_n~|~ c_n=r_0 e^{2\pi i n/8}, n \in \{0,1,...,7\}\big\}\)</span> with center <span>\(\omega_0 = 2 \pi c/(1600~\mathrm{nm})\)</span> and radius <span>\(r_0 = \omega_0\times10^{-2}\)</span>. For finite element degrees <span>\(d\)</span> higher than 5, the error saturates. For this reason, the differences between results for <span>\(d=5\)</span> and <span>\(d = 6\)</span> may depend on the hardware architecture.</p> <p>A larger radius <span>\(r = 4\times10^{13}\)</span> has been chosen for the optimization to include information from poles located further away from the frequency of interest. The target function <span>\(t(p_1,\dots,p_5) = -q_n \left(1 - \frac{(\omega_n-\omega_0)^2}{r^2} \right)\)</span>is minimized. The first factor is the negative <em>Q-</em>Factor and the second factor ensures that the target is zero at the boundary. If no eigenfrequency <span>\(\omega_n\)</span> is located inside the contour, the target is set to zero. For the purpose of this data publication some numerical parameters have been improved. This resulted in a faster convergence of the optimization.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.0 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p> <p>[2] Felix Binkowski, Lin Zschiedrich, Sven Burger, A Riesz-projection-based method for nonlinear eigenvalue problems, Journal of Computational Physics <strong>419</strong>, 109678 (2020), https://doi.org/10.1016/j.jcp.2020.109678</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[4] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p> <p>[5] Kirill Koshelev, Sergey Kruk, Elizaveta Melik-Gaykazyan, Jae-Hyuck Choi, Andrey Bogdanov, Hong-Gyu Park, Yuri Kivshar, Subwavelength dielectric resonators for nonlinear nanophotonics, Science <strong>367</strong>, 288 (2020), http://dx.doi.org/%2010.1126/science.aaz3985</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.