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Interactive Visualizations for: "Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties"
<p>This deposit includes 13 HTML 3D interactive visualizations of filaments and galaxies investigated in the accepted article, "<em>Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties</em>" by Castignani et al. (accepted, 20-Oct-2021).</p> <p>The specific files correspond to the filaments listed in Table 2 of the accepted manuscript:</p> <table align="left"> <caption>Tabulated HTML files and filaments</caption> <thead> <tr> <th scope="col">HTML File</th> <th scope="col">Filament (Table 2)</th> </tr> </thead> <tbody> <tr> <td> <p>SG_cube_Virgo_Serpens_Filament.html</p> </td> <td> <p>Serpens F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Coma_Berenices_Filament.html</p> </td> <td> <p>Coma Berenices F.</p> </td> </tr> <tr> <td> <p>SG_cube_VirgoIII_Filament.html</p> </td> <td> <p>VirgoIII F.</p> </td> </tr> <tr> <td> <p>SG_cube_Ursa_Major_Cloud.html</p> </td> <td> <p>Ursa Major Cloud</p> </td> </tr> <tr> <td> <p>SG_cube_NGC5353_4_Filament.html</p> </td> <td> <p>NGC5353/4 F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_Filament.html</p> </td> <td> <p>Leo Minor F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_B_Filament.html</p> </td> <td> <p>LeoII B F.</p> </td> </tr> <tr> <td> <p>SG_cube_Canes_Venatici_Filament.html</p> </td> <td> <p>Canes Venatici F</p> </td> </tr> <tr> <td> <p>SG_cube_W-M_Sheet.html</p> </td> <td> <p>W-M Sheet</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Draco_Filament.html</p> </td> <td> <p>Draco F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Bootes_Filament.html</p> </td> <td> <p>Bootes F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_B_Filament.html</p> </td> <td> <p>Leo Minor B F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_A_Filament.html</p> </td> <td> <p>LeoII A F.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>For each visualization galaxies within 2 Mpc are color-coded by the 3D local density, and galaxies with separations greater than 2 Mpc are shown with the grey points. The filament spine is shown with the black curve.</p> <p>The files were created with plotly.js v1.58.4.</p> <p> </p>
Datasets and results of the paper titled "Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"
<p>These are the <strong>input datasets</strong> and the <strong>results of the analyses</strong> reported on the paper titled <strong>"Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"</strong>.</p> <p><strong>Abstract:</strong> </p> <p>The aim of this paper is to study the role of citation network measures in the assessment of scientific maturity. Referring to the case of the Italian national scientific qualification (ASN), we investigate if there is a relationship between citation network indices and the results of the researchers’ evaluation procedures. In particular, we want to understand if network measures can enhance the prediction accuracy of the results of the evaluation procedures beyond basic performance indices. Moreover, we want to highlight which citation network indices prove to be more relevant in explaining the ASN results, and if quantitative indices used in the citation-based disciplines assessment can replace the citation network measures in non-citation-based disciplines. Data concerning Statistics and Computer Science disciplines are collected from different sources (ASN, Italian Ministry of University and Research, and Scopus) and processed in order to calculate the citation-based measures used in this study. Following, we apply classification models to estimate the effects of network variables. We find that network measures are strongly related to the results of the ASN and significantly improve the explanatory power of the models, especially for the research fields of Statistics. Additionally, citation networks in the specific sub-disciplines are far more relevant than those in the general disciplines. Finally, results show that the citation network measures are not a substitute of the citation-based bibliometric indices.</p> <p><strong>Code</strong></p> <p>The code to collect and process the data used in this paper is available on GitHub at <a href="https://github.com/DigitalDataLab/ASN16-18_CitationNetwork">https://github.com/DigitalDataLab/ASN16-18_CitationNetwork</a><strong>.</strong> </p> <p><strong>Dataset description</strong></p> <p>The files <strong>AdjacencyMatrix_01B1.csv</strong>, <strong>AdjacencyMatrix_09H1.csv</strong>, <strong>AdjacencyMatrix_13D1.csv</strong>, <strong>AdjacencyMatrix_13D2.csv</strong> and <strong>AdjacencyMatrix_13D3.csv</strong> are the citation matrices for Italian academics (i.e. ASN candidates and permanent positions in the Italian academic system) in the Recruitment Fields (RFs) 01/B1, 09/H1, 13/D1, 13/D2 and 13/D3, respectively.</p> <p>The files <strong>AdjacencyMatrix_CS.csv</strong> and <strong>AdjacencyMatrix_ST.csv</strong> are the citation matrices for the Italian academics in the Computer Science disciplines (i.e. RFs 01/B1 and 09/H1) and the Statistical disciplines (i.e. RFs 13/D1, 13/D2 and 13/D3), respectively.</p> <p>The files <strong>CS_01B1_1.csv, CS_09H1_1.csv, ST_13D1_1.csv, ST_13D2_1.csv</strong> and <strong>ST_13D3_1.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Full Professor (FP) level.</p> <p>The files <strong>CS_01B1_2.csv, CS_09H1_2.csv, ST_13D1_2.csv, ST_13D2_2.csv</strong> and <strong>ST_13D3_2.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Associate Professor (AP) level.</p> <p>The file <strong>Codebook.pdf</strong> is the codebook of the previous ten files.</p> <p>The file <strong>Appendix.pdf</strong> contains the final results of the stepwise logistic regressions computed for each level (i.e. Full Professor and Associate Professor) and Recruitment Field in the Computer Science and Statistics disciplines.</p> <p>The file <strong>NormalityAssessment.pdf</strong> contains the normality assessment of citation network indices. </p>
Experimental Results of the REWIRE FED4FIRE+ Open Call (OC) 9 Project
<p>This repository contains the detailed experimental results of the <strong>REWIRE <em>"Experimenting with SDN-based Adaptable Non-IP Protocol Stacks in Smart-City Environments"</em></strong> project.</p> <p>This work has received funding from the EU's Horizon 2020 research and innovation programme through the 9th open call scheme of the FED4FIRE+ (grant agr. no 732638)</p>
Results of the 3D detection of cracks in tested disc-shaped specimens
<p> </p> <p>3D images of fatigue crack obtained by laboratory tomography and synchrotron tomography within bi-disc specimens.</p>
CIRO experimental results
<p>This repository includes the experiment results, source code, and test data for <a href="https://www.mhlw.go.jp/content/3CS.pdf">Three Cs</a> risk inference, using the CIRO (COVID-19 Infection Risk Ontology) and <a href="http://www.hermit-reasoner.com/">HermiT</a>.</p> <ul> <li>results.csv: The results of the experiment.</li> <li>test_cases.csv: The test cases of the experiment.</li> <li>CIRO.owl: The COVID-19 Infection Risk Ontology.</li> <li>test_main.py: The source code of the experiment.</li> </ul> <p>Requirements:</p> <ul> <li>Python 3</li> <li><a href="https://owlready2.readthedocs.io/en/v0.36/index.html">Owlready2</a></li> <li><a href="https://rdflib.readthedocs.io/en/stable/">rdflib</a></li> </ul>
Destructive potential of planetary meteotsunami waves: ATAL ocean model results
<p> Based on the well-documented Hunga Tonga–Hunga Ha'apai volcano explosive eruption on 15 January 2022, we developed the "Atmospheric Tsunami Associated with Lamb waves" or ATAL ocean model and performed 12 realistic and process oriented numerical simulations to assess the sea-level hazards posed by planetary meteotsunami waves. Here, we provide the ATAL model results of maximum sea-levels during day 1 and day 2 after the eruption for:</p> <p>- the baseline simulation: trying to reproduce the event as realistically as possible</p> <p>- the 10 resonance simulations (_r_): trying to derive the speed of the Lamb waves which will generate the maximum resonance (i.e., Proudman resonance) in the ocean basins by dividing the baseline speed by r = 1.25, 1.40, 1.50, 1.60, 1.65, 1.75, 2.00, 3.00, 4.00, 5.00. The full Proudman resonance was obtained for r = 1.50</p> <p>- the amplification simulation (_amp_): trying to match the Proudman resonance amplification by multiplying by 10 the Lamb waves amplitudes</p>
SciKGTeX Scientific Contribution Metadata LaTeX Package User Evaluation Results & Analysis
<p>The responses and measured variables from 26 participants of the first user test of the SciKGTeX package.</p> <p><a href="https://github.com/Christof93/SciKGTeX">https://github.com/Christof93/SciKGTeX</a></p> <p>Also the raw text source for the evaluation tasks and the result analysis notebook with the results saved as tsv file.</p>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
The allocation of Chinese and Indian development finance in Nepal and its influence on local election results
<p>This realease contains the used datasets and calculations for my Bachelorthesis about Indian and Chinese allocation of Overall Development Assistance in Nepal.</p>
Project Information Model resulting from the on-site survey
<p>The On-site Analysis and Verification Service (ODAVS) is a service developed within the Encore project. It allows users to check any constructability issues regarding renovation projects of residential buildings by means of surveys facilitated by a mixed reality tool. </p> <p>This dataset includes two IFC files of the renovation studies developed by Univpm and JEA (both partners of Encore project) at JEA experimental building in Caceres, and assessed on-site on 2021 December 14th and 15th. The dataset also includes an XML file containing the list of 33 URLS pointing to audio files previously published on a different Zenodo dataset [1]. Note that the access to the Zenodo dataset [1] is restricted. The interested users must ask the dataset authors for permission to access the dataset itself. In the XML file, for each comment, the GUID of the IFC object referred by the audio comment itself is given.</p> <p>References</p> <p>[1] "Pictures and Videos Collected During ODAVS Activity", by Carbonari A. and Vaccarini M., DOI 10.5281/zenodo.6531860, URL: https://doi.org/10.5281/zenodo.6531860</p> <p> </p>
Trajan's entourage - Data and results
<p><strong>Description. </strong>This repository contains the data collected by Gaëtane Vallet regarding the entourage of Roman emperor Trajan, as well as the social networks and sequences extracted by Vincent Labatut from these data, and all the files resulting from their analysis.</p> <p>The data and the retrieval process are described in G. Vallet's theses (in French):</p> <div> <div> <div> <div> <div> <div> <div> <ol> <li>Vallet, G. <em>L'entourage de Trajan : étude prosopographique de l'entourage du prince de 98-117 apr. J.-C.</em>, First part of the Master's thesis, Avignon University, Human and Social Sciences Faculty, History Department, Avignon, France.</li> <li>Vallet, G. <em>Les princes et les hommes : carrières et réseaux dans l’entourage de Trajan de 98 à 118 après J.-C.</em>, Second part of the Master's thesis, Avignon University, Human and Social Sciences Faculty, History Department, Avignon, France.</li> </ol> </div> </div> </div> </div> </div> </div> </div> <p><strong>Source code. </strong>The associated R source code is available separately on GitHub : <a href="https://github.com/CompNet/TrajanNet">https://github.com/CompNet/TrajanNet</a></p> <p>The various archives available in this dataset match the output folders of the R scripts. See the GitHub page for more details.</p> <p><strong>Citation.</strong> If you use these data or this source code, please cite the second thesis above:</p> <p><code>@MastersThesis{Vallet2020,</code><br><code> author = {Vallet, Gaëtane},</code><br><code> title = {Les princes et les hommes : carrières et réseaux dans l'entourage de Trajan de 98 à 118 après J.-C.},</code><br><code> year = {2020},</code><br><code> type = {MA Thesis},</code><br><code> institution = {Avignon University},</code><br><code>}</code></p> <p> </p>
data-augmentation-ner-results
<p>Model evaluation results produced in the context of evaluating data augmentation for Named Entity Recognition over the German legal domain.</p> <p>Detailed information can be found on the <a href="https://github.com/fusion-jena/data-augmentation-ner-legal">Github page</a>.</p>
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>
Crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), GIXD analysis results: diffraction features and crystal structure
<p>Analysis result of an <em>in-situ</em> measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate.</p> <p>This dataset contains the positions, sizes, and integrated intensities of extracted diffraction peaks with 0.1s time resolution.</p> <p>For crystal structure matching, the provided CIF file (CCDC 1446529) was used.</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Annotation and Orthofinder results for three Mytilus species genomes.
<p>Annotations and Orthofinder results for three Mytilus species genomes: accessions JAKGDF000000000 (MgalMED), JAKGDG000000000 (MeduEUS), and JAKGDH000000000 (MeduEUN).</p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Cascading effects augment the direct impact of CO2 on phytoplankton growth in a biogeochemical model, links to model results
<p>This dataset provides the output of eight model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2022). In addition to information on the mesh, the dataset contains 1) 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, calcification, grazing rates, calcite concentrations, zooplankton biomass, export fluxes as well as CO<sub>2(aq)</sub>, HCO<sub>3</sub><sup>-</sup> and nutrient concentrations, and 2) a time series of global and North Atlantic coccolithophore biomass, temperature, and CO<sub>2(aq)</sub> concentrations from 1958 to 2018.</p> <p>File names refer to the Figures and Tables in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
COALMOD-World 2.0 data, results, figures for: Stranded assets and early closures in global coal mining under 1.5°C
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein (2023): Stranded assets and early closures in global coal mining under 1.5°C (doi.org/10.1088/1748-9326/acb0e5) </p> <p>With the input data files and the GAMS scenario file the model (https://doi.org/10.5281/zenodo.7077678) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Sweden
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
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.