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3,172 results for “Publications”
Supplementary Data for Publication related to NFFA proposal ID-509
<p>This is supplementary data for the publication "Ordered Transfer from 3D-Oriented MOF Superstructures to Polymeric Films: Microfabrication, Enhanced Chemical Stability, and Anisotropic Fluorescent Patterns", DOI: 10.1002/adma.202404384, related to the NFFA proposal ID-509. </p>
Accompanying data for publication: "Learning the Optimal Power Flow: Environment Design Matters"
<p>All the data created for the publication "Learning the Optimal Power Flow: Environment Design Matters" by Wolgast and Nieße. The dataset contains all training runs performed, including the final neural network weights, meta-data about the training run, and various metrics during the course of training, which were used to generate the results and plots. The source code to re-produce the plots for the publication (and everything else) can be found on GitHub: https://github.com/Digitalized-Energy-Systems/rl-opf-env-design</p>
Fig. 1 in Trichinella T9 in wild bears in Japan: Prevalence, species/genotype identification, and public health implications
Fig. 1. Map of Japan (A) illustrating the bear capture sites in this study (B). The red shapes indicate the capture sites for brown bears (Ursus arctos) and blue for Japanese black bears (Ursus thibetanus japonicus). The filled shapes indicate the locations of Trichinella-positive animals: A, Numata, Sorachi Subprefecture; B, Shimamaki, Shiribeshi Subprefecture; and C, Kaminokuni, Hiyama Subprefecture; D, Shiwa, Iwate Prefecture. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Research data supporting the publication "Hierarchy of Topological Transitions in a Network Liquid"
<p>This dataset contains the data used to produce each of the figures in the manuscript "Hierarchy of Topological Transitions in a Network Liquid" published in PNAS. The dataset includes configuration files for the dendrimer system generated via NPT Monte Carlo simulations and the topological analysis of the networks formed by the dendrimer particles.</p>
Data produced and used in the publication "Modelling chemical advection during magma ascent" by Dominguez et al., 2024.
<p>Data produced by the numerical models used for the study titled "Modelling chemical advection during magma ascent", by Dominguez et al., 2024. The code used to produce the data is available at https://zenodo.org/records/12624639.</p> <p>Each file contains the data of one numerical model store as HDF5, with 4 timesteps and the initial conditions.</p>
Data supporting publication: Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces
<p>Dataset for "Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces" Figure 1 to Figure 5.</p>
Data for Publication - Impact of agricultural systems on arbuscular mycorrhizal fungi community composition in Robusta coffee roots in the Democratic Republic of Congo
<p>Data used for the publication:</p> <p>"Impact of agricultural systems on arbuscular mycorrhizal fungi community composition in Robusta coffee roots in the Democratic Republic of Congo" - Ieben Broeckhoven, Arne Devriese, Olivier Honnay, Roel Merckx, and Bruno Verbist</p>
Dataset for the publication "Reversing the magnetization of 50-nm-wide ferromagnets by ultrashort magnons in thin-film Yttrium Iron Garnet"
<p>Dataset belonging to the manuscript "Reversing the magnetization of 50-nm-wide ferromagnets by ultrashort magnons in thin-film Yttrium Iron Garnet" published in Nanoscale Horizons, doi: <a href="https://doi.org/10.1039/D4NH00095A">https://doi.org/10.1039/D4NH00095A</a></p> <p>Every specific folder contains a text file that explains the measurement parameters and file formats.</p> <p>Abstract:</p> <p>Spin waves (magnons) can enable neuromorphic computing by which one aims at overcoming limitations inherent to conventional electronics and the von Neumann architecture. Encoding magnon signal by reversing magnetization of a nanomagnetic memory bit is pivotal to realize such novel computing schemes efficiently. A magnonic neural network was recently proposed consisting of differently configured nanomagnets that control nonlinear magnon interference in an underlying yttrium iron garnet (YIG) film [Papp et al., Nature communications, 2021, 12, 6422]. In this study, we explore the nonvolatile encoding of magnon signals by switching the magnetization of periodic and aperiodic arrays (gratings) of Ni81Fe19 (Py) nanostripes with widths w between 50 nm and 200 nm. Integrating 50-nm-wide nanostripes with a coplanar waveguide, we excited magnons having a wavelength λ of ≈100 nm. At a small spin-precessional power of 11 nW, these ultrashort magnons switch the magnetization of 50-nm-wide Py nanostripes after they have propagated over 25 μm in YIG. We also demonstrate the magnetization reversal of nanostripes patterned in an aperiodic sequence. We thereby show that the magnon-induced reversal happens regardless of the width and periodicity of the nanostripe gratings. Our study enlarges substantially the parameter regime for magnon-induced nanomagnet reversal on YIG and is important for realizing in-memory computing paradigms making use of magnons with ultrashort wavelengths at low power consumption."</p>
nd-crane/trusted_ke: OMIn Dataset v1.0.0 - Initial Public Release
<h1>Release Notes: OMIn Dataset v1.0.0</h1> <p>We are excited to announce the release of the <strong>Operations and Maintenance Intelligence (OMIn) Dataset</strong>, a key contribution from our recent research publication. OMIn is the first open-source dataset curated specifically for knowledge extraction (KE) in the operation and maintenance domains.</p> <h3>Overview</h3> <p>The OMIn Dataset is based on raw FAA Accident/Incident data and has been meticulously curated to support KE in operations and maintenance. It features detailed textual descriptions of maintenance incidents, with particular emphasis on mentions of aircraft systems and domain-specific shorthand. This dataset includes:</p> <ul> <li><strong>Gold Standards</strong>: Prepared for Named Entity Recognition (NER), Coreference Resolution (CR), and Named Entity Linking (NEL).</li> <li><strong>Textual Descriptions</strong>: Rich narratives of maintenance incidents, long enough to provide context and valuable information for KE tasks.</li> <li><strong>Structured Data</strong>: Information on aircraft details, failure codes, and dates, enabling future work on integrated KE approaches that combine structured data with natural language text.</li> </ul> <h3>Significance</h3> <p>OMIn expands the portfolio of resources available in the operation and maintenance domains by offering comprehensive records on a variety of subject matters. It is designed to be a valuable baseline dataset due to its:</p> <ul> <li><strong>Variety and Depth</strong>: The dataset covers a wide range of incidents, each described in detail to offer context and facilitate knowledge extraction.</li> <li><strong>Versatility</strong>: While OMIn is rooted in aviation maintenance incident data, its structure and content make it applicable to many records or logs in the operation and maintenance sectors.</li> </ul> <h3>Community Invitation</h3> <p>By releasing OMIn, we aim to support the broader community in the maintenance and manufacturing domains. We invite researchers, practitioners, and developers to collaborate on this open-source KE dataset, helping to build a robust resource that advances the state of the art in the field.</p> <h3>How to Access</h3> <p>You can download the OMIn dataset directly from this repository or access it through Zenodo, where it has been assigned a DOI for reference in academic publications.</p>
Data for the publication "MgB2Se4 Spinels (B = Sc, Y, Er, Tm) as Potential Mg-Ion Solid Electrolytes – Partial Ionic Conductivity and the Ion Migration Barrier"
<h1><strong>Description Datasets</strong></h1> <p>Datasets are obtained from X-ray diffraction (XRD), Rietveld analysis, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), nuclear magnetic resonance (NMR) spectroscopy, electrochemical impedance spectroscopy (EIS), chronoamperometry (CA), chronopotentiometry (CP) and linear sweep voltammetry (LSV).</p> <h1><strong>Abstract</strong></h1> <p>The magnesium chalcogenide spinel MgSc~2~Se~4~ with high Mg-ion room-temperature conductivity has recently attracted interest as solid electrolyte for magnesium ion batteries. Its ionic/electronic mixed-conducting nature and the influence of the spinel composition on the conductivity and Mg^2+^ migration barrier are yet not well understood. Here, results from a combined experimental and computational study on four MgB~2~Se~4~ spinels (B = Sc, Y, Er, Tm) are presented. The room-temperature ionic conductivities (<em>σ</em>~ion~ = 2x10^–5^–7x10^–5^ S cm^–1^) of the spinels are accurately measured, as electron transport is effectively suppressed by purely Mg-ion conducting electrode interlayers. Using the same approach, reversible Mg plating/stripping as well as good electrochemical stability are achieved. Driven by the good accordance of the computationally and experimentally obtained Mg^2+^ migration barriers <em>E</em>~a~(th) and <em>E</em>~a~, respectively, further periodic density functional calculations are performed on the MgB~2~Se~4~ spinel system, revealing the role of trigonal distortion on the migration path geometry and <em>E</em>~a~(th). These findings provide deeper understanding how to reach small Mg^2+^ migration barriers <em>E</em>~a~ in the MgB~2~Se~4~ spinels.</p>
Publications of professors and teachers of the Ukrainian Free University (1921 - 1925), placed in the Open Access
<p>The publications of scientists of the Ukrainian Free University for the period from 1921 to 1925 inclusive, placed in the Open Access with the indication of electronic libraries in which they were placed, are presented. </p>
Dataset related to the publication "Measurement and assignment of J = 5 to 9 rotational energy levels in the 9070-9370 cm-1 range of methane using optical frequency comb double-resonance spectroscopy"
<p>The files contain the normalized interleaved double-resonance spectra recorded with four different pump transitions, indicated in the file name. The first column is the wavenumber, the second column is the transmission intensity.</p>
Computational results data for the assoziated publication "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics"
<p>This repository contains two Excel tables with computational results for our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics". Each Excel table includes multiple worksheets, each representing different scenarios and models evaluated in our study.</p> <p><strong>Worksheets Overview</strong></p> <p>Each Excel table contains the following worksheets:<br>1. <strong>ML</strong>: Results for the "ML" big M values.<br>2. <strong>MH</strong>: Results for the "MH" big M values.<br>3. <strong>MF</strong>: Results for the "MF" big M values.<br>4. <strong>Path</strong>: Results for the path model.<br>5. <strong>FMInstances</strong>: Results from applying our models on the original Fontaine and Minner (2018) instances.</p> <p><strong>Columns Description</strong></p> <p>Each worksheet contains the following columns:</p> <p>- <strong>Name of Instance</strong>: A complex string with the identifier of the used instance. The relevant part is "_RXXX_", where XXX is the random seed used to generate the instance.<br>- <strong>Number users</strong>: The number of users/commodities in the network.<br>- <strong>B:</strong> The budget (always set to infinity in our instances).<br>- <strong>GUROBI_RUNTIME</strong>: The time limit set for the computations.<br>- <strong>Modelkind</strong>: The type of model used. Possible values are:<br> - INDICATOR: Compact model.<br> - FMbenders: Benders model from Fontaine and Minner (2018).<br> - ICM: Benders-like cuts.<br> - PathModel: Path enumeration model.<br>- <strong>BigM computation</strong>: Time required to compute all the big M values used (not included in the time limit).<br>- <strong>runtime</strong>: Runtime of the selected model.<br>- <strong>BBnodes</strong>: Number of nodes in the Branch & Bound tree.<br>- <strong>gap</strong>: Gap reported by Gurobi after reaching the time limit.<br>- <strong>Cuts BLC</strong>: Number of Benders-like cuts included.<br>- <strong>Time BLC</strong>: Time required for separating Benders-like cuts.<br>- <strong>M improve BLC</strong>: Frequency of improvements to a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Mcutoff_AVE</strong>: Average (non-zero) improvement of a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Cuts FMBenders</strong>: Number of Benders cuts generated in the Fontaine and Minner (2018) model.<br>- <strong>Time FMBenders</strong>: Time required to generate the Benders cuts in the Fontaine and Minner (2018) model.<br>- <strong>Runtime path enum</strong>: Time required to enumerate all paths for the path-based model (not included in the time limit).<br>- <strong>Average Num Path</strong>: Average number of paths generated for a single commodity/user. Multiply this value by the number of users to obtain the absolute number of paths generated.</p> <p><strong>Note on FCP</strong></p> <p>All the results found for the instances already had integer flow solutions. Additionally, we conducted experiments where we explicitly forced the solutions to be integer for the Benders-like cuts model. We observed that the runtimes remained the same, with only some natural insignificant hardware-induced fluctuations. Therefore, we omit reporting these results again.</p> <p><br>For further information or questions, please refer to our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics" or contact the authors.</p>
Data and code for the publication: "Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines"
<p>A. Feinberg, Aug 2024</p> <p>arifeinberg@gmail.com</p> <p> </p> <p>Essential data and code for the publication: Feinberg et al. : Unexpected anthropogenic emission decreases are required to explain recent atmospheric mercury concentration declines</p> <p> </p> <p>The directories include:</p> <p>1) analysis<strong>_</strong>plotting<strong>_</strong>scripts/ - all analysis scripts used to analyze observations, produce input data, and plot figures for paper</p> <p>2) GC<strong>_</strong>code/ - Archived GEOS-Chem code used to simulate the runs in this paper</p> <p>3) GC<strong>_</strong>data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:</p> <p>BASE - run2021</p> <p>BASE+LEG - run2022</p> <p>DEC<strong>_</strong>LEG<strong>_</strong>ONLY - run2024</p> <p>ZHANG23 - run2025</p> <p>DEC<strong>_</strong>ANT<strong>_</strong>NH - run2026</p> <p> </p> <p>4) input<strong>_</strong>data/ - input data used to run GEOS-Chem</p> <p> </p> <p>Please refer to other README.md files within sub-directories and contact me for any questions.</p>
The PANORAMA Challenge: Public Training and Development Dataset (2)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>2</strong> <strong>out of 4</strong></em></p>
The PANORAMA Challenge: Public Training and Development Dataset (1)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>1</strong> <strong>out of 4</strong></em></p>
Dataset for the publication "Identification of plasticity-induced crack closure by using high-resolution digital image correlation"
<p>This repository publishes the data generated in the article "Identification of plasticity-induced crack closure by using high-resolution digital image correlation" (see arxiv preprint <a href="https://arxiv.org/html/2409.02560v1">Plasticity-induced crack closure identification during fatigue crack growth in AA2024-T3 by using high-resolution digital image correlation (arxiv.org)</a>)</p> <p>This repository is structured with the following subfolders:</p> <ul> <li><strong>0_fe_data: </strong>contains the displacement field of the free surface of the 3D finite element model that were used to determine the crack opening curves and, in the following, the crack opening value Kop</li> <li><strong>1_hrdic_data: </strong>contains the high-resolution DIC displacement field data at a crack length of 27.8 mm at different load levels, starting from minimum load 1.5 kN to maximum load 15 kN</li> </ul>
Further diagrams of the publication Schürmann & Langer 2024 (Astronomy & Astrophysics)
<h2>Content/Naming convention</h2> <p>Here are three groups of image files: HRD_??.png, AngMom_??.png, and WR?_b1_50.png. Each is an extension to one of the figures in Schürmann & Langer 2024.</p> <h3><br>HRD_??.png</h3> <p>Same as Fig. 1 (bottom), but for different initial masses (see file name) for various accretion rates (indicated by colour).</p> <h3><br>AngMom_??.png</h3> <p>Same as Fig. 8, but the ejected material carries either single (?=1) or double (?=2) the specific orbital angular momentum of the donor (AngMom_a?.png), of the accretor (AngMom_b?.png), of the orbit (AngMom_h?.png), or none (AngMom_h0.png).</p> <p>AngMom_a1.png: beta = eta = 0, alpha = - epsilon, A = 1<br>AngMom_a2.png: beta = eta = 0, alpha = - epsilon, A = 2<br>AngMom_b1.png: alpha = eta = 0, beta = - epsilon, B = 1<br>AngMom_b2.png: alpha = eta = 0, beta = - epsilon, B = 2<br>AngMom_h0.png: A = B = H = 0<br>AngMom_h1.png: alpha = beta = 0, eta = - epsilon, H = 1<br>AngMom_h2.png: alpha = beta = 0, eta = - epsilon, H = 2</p> <h3><br>WR?_b1_50.png</h3> <p>Same as Fig. 9, but for AB 3, AB 6, AB 7, AB 9. We assumed a WR progenitor mass of 50Msol, which is a typical number (see Table 3), and since the area of the contract-avoiding region varies not so strongly with mass. WR_key.png is the legend.</p>
Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning (Dataset)
<div> <h2>Overview</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#overview"></a></div> <p>This repository contains the code and resources for the project titled <strong>"Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning"</strong>. The goal of this project is to identify regions where individuals are living under precarious conditions and facing neglected basic needs, a situation often seen in Brazil. This concept is referred to as "human vulnerability" and is exemplified by families living in inadequate shelters or on the streets in both urban and rural areas.</p> <p>Focusing on the Federal District of Brazil as the research area, this project aims to develop two novel public datasets consisting of satellite images. The datasets contain imagery captured at 50m and 100m scales, covering regions of human vulnerability, traditional areas, and improperly disposed waste sites.</p> <p>The project also leverages these datasets for training deep learning models, including <strong>YOLOv7</strong> and other state-of-the-art models, to perform image segmentation. A comparative analysis is conducted between the models using two training strategies: training from scratch with random weight initialization and fine-tuning using pre-trained weights through <strong>transfer learning</strong>.</p> <div> <h3>Key Achievements</h3> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#key-achievements"></a></div> <ul> <li>Two new satellite image datasets focusing on human vulnerability and improperly disposed waste sites, available in public domains.</li> <li>Comparison of image segmentation models, including <strong>YOLOv7</strong> and <strong>Segmentation Models</strong>, with performance metrics.</li> <li>Best F1-scores: 0.55 for <strong>YOLOv7</strong> and 0.64 for <strong>Segmentation Models</strong>.</li> </ul> <p>This repository provides the code, models, and data pipelines used for training, evaluation, and performance comparison of these deep learning models.<br><br></p> <div> <h2>Citation (Bibtex)</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#citation-bibtex"></a></div> <pre><code>@TECHREPORT {TechReport-Julia-Laura-HumanVulnerability-2024, author = "Julia Passos Pontes, Laura Maciel Neves Franco, Flavio De Barros Vidal", title = "Detecção de Áreas com Atividades de Vulnerabilidade Humana utilizando Imagens Públicas de Satélites e Aprendizagem Profunda", institution = "University of Brasilia", year = "2024", type = "Undergraduate Thesis", address = "Computer Science Department - University of Brasilia - Asa Norte - Brasilia - DF, Brazil", month = "aug", note = "People living in precarious conditions and with their basic needs neglected is an unfortunate reality in Brazil. This scenario will be approached in this work according to the concept of \"human vulnerability\" and can be exemplified through families who live in inadequate shelters, without basic structures and on the streets of urban or rural centers. Therefore, assuming the Federal District as the research scope, this project proposes to develop two new databases to be made available publicly, considering the map scales of 50m and 100m, and composed by satellite images of human vulnerability areas, regions treated as traditional and waste disposed inadequately. Furthermore, using these image bases, trainings were done with the YOLOv7 model and other deep learning models for image segmentation. By adopting an exploratory approach, this work compares the results of different image segmentation models and training strategies, using random weight initialization (from scratch) and pre-trained weights (transfer learning). Thus, the present work was able to reach maximum F1 score values of 0.55 for YOLOv7 and 0.64 for other segmentation models." } </code></pre> <div> </div> <div> <h2>License</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#license"></a></div> <p>This project is licensed under the MIT License - see the LICENSE file for details.</p>
Data for publication "The Thermosphere was Poorly Predictable not Only During but also Before and After the Starlink Storm on 3-4 February 2022"
<p>This dataset are used to plot figures in the article "The Thermosphere was Poorly Predictable not Only During but also Before and After the Starlink Storm on February 3-4, 2022". Data files in CSV (comma-separated values) format contain modeling and observational values. Modeling values obtained from the Field Line Interhemispheric Plasma (FLIP) model and Arctic MERRA-2 Wind model. Observational values consist the ionosonde measurments, planetary geomagnetic (Kp) and solar activity indices (F10.7), variations of the solar wind parameters. Data files contain data for the period from February 1 to 9, 2022 and from December 21 to 23, 2021. </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.