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9,152 results for “Networking”
The public goods game on multiplex networks
<p>The code and data used to create the figures in chapters 3-7 in the thesis "The public goods game on multiplex networks". The data is labelled for which figure it corresponds to.</p>
Supplementary Data: The Benefits of Cooperation in a Highly Renewable European Electricity Network
<p>Supplementary Data</p> <p><em>The Benefits of Cooperation in a Highly Renewable European Electricity Network</em><br> <em>doi:10.1016/j.energy.2017.06.004</em><br> <em>arXiv:1704.05492</em></p> <p>The files in this record contain the model-specific code, input data, and output data considered in the Benefits of Cooperation paper.</p> <p>You are welcome to use the provided data under the given open-source licence, and if you do please cite the paper <em>doi:10.1016/j.energy.2017.06.004</em>.<br> Please note that the derivation of the data in data/renewables/ is not open, because it uses the REatlas software [7] which has a closed source server part. (There is an free software implementation of the REatlas at https://github.com/FRESNA/atlite but it wasn't ready in time to be used for this dataset.)</p> <p>The code that is required to generate the output data consists of</p> <ul> <li>the python code opt_ws_network.py that builds and runs the PyPSA [0] model</li> <li>a SLURM script parameter_batch.py to run the model with different parameters</li> <li>a YAML file options.yml with the default parameter settings</li> </ul> <p>The code heavily relies on the python package <em>vresutils</em> which is available at https://github.com/FRESNA/vresutils</p> <p>The record also contains the input data in the data/ directory. They are described in detail in the paper, but a short summary is provided here:</p> <ul> <li><strong>costs</strong>: cost and other input parameter assumptions, see <em>Table 1</em> in the paper.</li> <li><strong>graph</strong>: the network topology is given by a list of nodes (country names) and a list of edges connecting two nodes. Based on [1,2].</li> <li><strong>hydro</strong>: hydro generation data provided by the Restore2050 project [3] <ul> <li>inflow/: contains a csv files with daily inflow data for each country</li> <li>emil_hydro_capas.csv: country-scale power and energy capacity</li> <li>ror_ENTSOe_Restore2050.csv: the share of run-of-river of the total hydro generation, from ENTSO-E [4] or if unavailable from [3]</li> </ul> </li> <li><strong>load</strong>: hourly country-scale consumption for 2011 from ENTSO-E [5]</li> <li><strong>renewables</strong>: generation potentials for the renewable technologies onshore wind, offshore wind, and solar per country based on historic weather data [6]. The jupyter-notebook europe_renewables_potentials.ipynb describes the data generation and uses the REatlas software [7] which has open-source client but closed-source server software. The used cutout can therefore not be made available here, but is solely based on data from [8]. The processed data are in: <ul> <li>store_p_nom_max/: installation potential per technology per region</li> <li>store_o_max_pu_betas/: hourly maximum generation per unit of capacity per technology per region</li> </ul> </li> </ul> <p>The output data generated by the model is in sub-folders of the results/ directory following the naming scheme [costsource]-CO[CO2costs]-T[timerange]-[technologies]-LV[linevolume]_c[crossover]_base_[costsource]_solar1_7_[formulation]-[startdate]/, where</p> <ul> <li>costsource = diw2030</li> <li>CO2costs = 0</li> <li>timerange = 1_8761</li> <li>technologies = wWsgrpHb</li> <li>linevolume = [float], None (line volume constraint of float * 5e8 TWkm, or optimised line volume)</li> <li>crossover = 0 (deactivated the cross-over phase of the Gurobi optimiser)</li> <li>formulation = angles, [blank] (power flow formulations: 'angles', or 'cycles')</li> <li>startdate = time the optimisation was started</li> </ul> <p>Footnotes</p> <p>[0] https://pypsa.org/ , https://doi.org/10.5281/zenodo.582307</p> <p>[1] S Becker, Transmission grid extensions in renewable electricity systems, PhD thesis (2015)</p> <p>[2] ENTSO-E, Indicative values for Net Transfer Capacities (NTC) in Continental Europe. European Transmission System Operators, 2011, https://www.entsoe.eu/publications/market-reports/ntc-values/ntc-matrix/Pages/default.aspx, accessed Jul 2014.</p> <p>[3] A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann, Simulation of renewable feed-in for power system studies, RESTORE 2050 project report, https://doi.org/10.5281/zenodo.804244</p> <p>[4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016), https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show</p> <p>[5] https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package</p> <p>[6] D. Heide, M. Greiner, L. Von Bremen, C. Hoffmann, Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation, Renewable Energy 36 (9) (2011) 2515–2523. https://doi.org/10.1016/j.renene.2011.02.009</p> <p>[7] G. B. Andresen, A. A. Søndergaard, M. Greiner, Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis, Energy 93, Part 1 (2015) 1074 – 1088. https://doi.org/10.1016/j.energy.2015.09.071</p> <p>[8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1</p>
US Water Network Observed and Inferred Flows
<p>This dataset provides a synthesized national record of river gauges. It includes inferred flows which are based on the structure of the river network and empirical flow relationships. In total, the network contains 22619 gauges (or virtual junction gauges) and over 1 million years of monthly data.</p> <p>The dataset is provide as a saved data file for R, flowdata.RData, containing two variables: nodes and allflow.</p> <p>`nodes` describes each gauges or virtual junction gauge in the water flow network, representing a combination of gauges from multiple sources. The source is specified by the `collection` column, as follows:</p> <ul> <li>`rivdis`: From the RivDIS dataset, at http://iridl.ldeo.columbia.edu/SOURCES/.UNH/.CSRC/.RivDIS/index.html?Set-Language=en</li> <li>`usgs`: GAGES II river gauge</li> <li>`reservoir`: National Inventory of Dams reservoir</li> <li>`usgsres`: USGS gauged reservoir</li> <li>`canal`: Canal cross county boundaries</li> <li>`junction`: Junction node added to capture the structure of rivers from HydroSHEDS</li> </ul> <p>The `colid` specifies the ID of the gauge within its collection, and combined `collection` and `colid` are unique. The location of the gauge is specified by the latitude `lat` and longitude `lon`. The elevation is provided in meters in column `elev`.</p> <p>The flow data is contained in the `allflow` matrix. This matrix has a column for each node (as many columns as there are rows in `nodes)`, and a row for each month starting from Jan. 1915. All the flows are in units of m^3 / s.</p>
IMP 1.0 Filtered Human Functional Network
<p>This is the filtered human functional network from the IMP webserver. It was downloaded from http://imp.princeton.edu/download/.</p>
Photometer Network - May - 2017
<p>Dataset with the measurements taken by the STARS4ALL photometers network</p>
Photometer Network - June - 2017
<p>Dataset with the measurements taken by the STARS4ALL photometers network</p>
Dataset for a Mouse and Rat heart trancriptomic and co-expression network analysis
<ul> <li>mouse_heart_data and rat_heart_expression contain GEO expression matrix for mouse and rat experiments.</li> <li>gse_gsm_mouse.txt and gse_gsm_rat.txt contain experiment IDs and series IDs from GEO.</li> <li>heart_quantNormData_mouse.tsv and heart_quantNormData_rat.tsv contain normalised expression matrices.</li> <li>heart_quantNormData_mouse_1sd.tsv and heart_quantNormData_rat_1sd.tsv contain the normalised expression matrices restricted to genes with a standard deviation higher than 1.</li> <li>fileForSCHypeThreshold0.5_heart.txt and fileForSCHypeThreshold0.75_heart.txt are the input for SCHype. schype_output_0.5th_heart.nodes.txt, schype_output_0.5th_heart.edges.txt, schype_output_0.75th_heart.nodes.txt and schype_output_0.75th_heart.edges.txt are the outputs.</li> <li>geneLists.zip contains gene lists used for ontology analysis (ENSEMBL gene id)</li> </ul>
The FORTH-TRACE dataset for human activity recognition of simple activities and postural transitions using a Body Area Network
<p>The dataset is collected from 15 participants wearing 5 Shimmer wearable sensor nodes on the locations listed in Table 1. The participants performed a series of 16 activities (7 basic and 9 postural transitions), listed in Table 2.</p> <p>The captured signals are the following:</p> <ul> <li>3-axis accelerometer</li> <li>3-axis gyroscope</li> <li>3-axis magnetometer</li> </ul> <p>The sampling rate of the devices is set to 51.2 Hz.</p> <p>DATASET FILES</p> <p>The dataset contains the following files:</p> <ul> <li>partX/partXdev1.csv</li> <li>partX/partXdev2.csv</li> <li>partX/partXdev3.csv</li> <li>partX/partXdev4.csv</li> <li>partX/partXdev5.csv</li> </ul> <p>Where X corresponds to the participant ID, and numbers 1-5 to the device IDs indicated in Table 1.</p> <p>Each .csv file has the following format:</p> <ul> <li>Column1: Device ID</li> <li>Column2: accelerometer x</li> <li>Column3: accelerometer y</li> <li>Column4: accelerometer z</li> <li>Column5: gyroscope x</li> <li>Column6: gyroscope y</li> <li>Column7: gyroscope z</li> <li>Column8: magnetometer x</li> <li>Column9: magnetometer y</li> <li>Column10: magnetometer z</li> <li>Column11: Timestamp</li> <li>Column12: Activity Label</li> </ul> <p>Table 1: LOCATIONS</p> <ol> <li>Left Wrist</li> <li>Right Wrist</li> <li>Torso</li> <li>Right Thigh</li> <li>Left Ankle</li> </ol> <p>Table 2: ACTIVITY LABELS</p> <p>(Arrows (->) indicate transitions between activities)</p> <ol> <li>stand</li> <li>sit</li> <li>sit and talk</li> <li>walk</li> <li>walk and talk</li> <li>climb stairs (up/down)</li> <li>climb stairs (up/down) and talk</li> <li>stand -> sit</li> <li>sit -> stand</li> <li>stand -> sit and talk</li> <li>sit and talk -> stand</li> <li>stand -> walk</li> <li>walk -> stand</li> <li>stand -> climb stairs (up/down), stand -> climb stairs (up/down) and talk</li> <li>climb stairs (up/down) -> walk</li> <li>climb stairs (up/down) and talk -> walk and talk</li> </ol>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
PYRAMID METEOROLOGICAL NETWORK - EVK2CNR
<p>The Pyramid Meteorological Network, located in Himalaya (Nepal), on the southern slopes of Mt. Everest is composed of 7 meteorological stations located between 2660 and 7986 m a.s.l., which have collected continuous climatic data during the last 30 years (1994-2023). These data are here available for download and specific for each parameter and each station on the dedicated Geoportal:</p> <p>https://geoportal.mountaingenius.org/portal/index.php/data/</p> <p> </p>
Universal microbial network decomposes mammals despite varied climate, location, and seasonal influence
<p>Microbial breakdown of organic material is one of the most important processes on earth, yet enormous knowledge gaps exist about its controls. We demonstrate that a universal, inter-kingdom microbial network assembles in response to nutrient-rich, terrestrial mammalian decomposition, despite selection effects of location, climate and season. We created the first metagenome-assembled genome library from mammalian decomposition-associated soils and combined it with metabolomics to identify a microbial decomposer network that interacts by cross-feeding to efficiently metabolize labile decomposition products. The key fungal and bacterial decomposers appear unique to the breakdown of terrestrial cadavers, and are rare in relative abundance across non-decomposition environments. Blow flies are suggested as an important decomposer vector and the observed lockstep of microbial interactions underlies a robust microbial forensic tool for predicting the time since death. </p>
SIF datasets (50 m) and demos of network structure designs on the study of the STP-SIF issue
<p>The SIF datasets (i.e., SIF2019 and SIF2020) accompany the paper "Regional-Scale Cotton Yield Forecast via Data-Driven Spatio-Temporal Prediction (STP) of Solar-Induced Chlorophyll Fluorescence (SIF)" that was published in <a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425723004121">Remote Sensing of Environment</a> on October 20, 2023. They have a spatial resolution of about 50 m and a monthly temporal resolution. Each of them has seven bands, corresponding to April to October. Please refer to our previous work, "Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network", which was published in <a href="https://www.sciencedirect.com/science/article/pii/S0168169922005737">Computers and Electronics in Agriculture</a> on August 14, 2022, for the development and detailed description.</p><p>The geographic reference (ESPG: 4326 (WGS_1984)) is the same for the two dataset, conforming to that in the geotiff file.</p><p><strong>Citation</strong>:</p><p>[1] Kang, X., Huang, C., Zhang, L., Wang, H., Zhang, Z., Lv, X., 2023. Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF). Remote Sensing of Environment 299, 113861. doi:10.1016/j.rse.2023.113861</p><p>[2] Kang, X., Huang, C., Zhang, L., Zhang, Z., Lv, X., 2022. Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network. Computers and Electronics in Agriculture 201, 107260. doi:10.1016/j.compag.2022.107260</p><p>[3] Kang, X., Huang, C., Chen, J.M., Lv, X., Wang, J., Zhong, T., Wang, H., Fan, X., Ma, Y., Yi, X., Zhang, Z., Zhang, L., Tong, Q., 2023. The 10-m cotton maps in Xinjiang, China during 2018-2021. Sci Data 10, 688. doi:10.1038/s41597-023-02584-3</p><p>[4] Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., Tong, Q., 2023. Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science 13, 1048479. doi:10.3389/fpls.2022.1048479</p>
Social network structure is robust to parasite induced changes in contact behavior of domestic sheep
<p>Understanding how parasitism may affect social behavior and social networks is key to understanding the impact of infection on a population. Infection can disrupt social networks by altering the behavior of both infected individuals (e.g. by reducing activity) and the behavior of uninfected individuals (e.g. avoiding sick individuals), both of which can <span>have an impact on social group dynamics and parasite transmission</span>. Here we test experimentally how parasitism affects social contact behavior and social network structure using a common parasite infection of sheep. Three treatment groups, each with 4 replicate social groups were established (i) Parasitised; all lambs were infected with a parasitic nematode, (ii) Non-parasitised; all lambs remained uninfected (iii) Mixed; part of each group were infected, and part of the group remained uninfected. Contact behaviours of each individual were recorded using proximity loggers during four phases of infection (pre-parasite, pre-patent, patent-parasite, post-parasite). We found infected individuals in the parasitised and mixed groups reduced contact frequency following infection. Infected individuals in mixed groups however reduced contact frequency to a greater extent than infected animals in the fully parasitised group. D<span>espite the reduction in contacts between infected animals in the mixed group, the social network structure was unaffected, as non-infected individuals maintained pre-parasite levels of social interactions with their infected conspecifics. </span><span>These results demonstrate </span>how infection can impact the social behavior of all animals within a group, and how the expression of behavioral change may depend on the parasitic status of all group members and the response of uninfected conspecifics.</p>
Simulation results of routing algorithms for multilayer networks
<p>Most current routing protocols are based on path computation algorithms in graphs (e.g., Dijkstra, Bellman-Ford, etc.). These algorithms have been studied for a long time and are very well understood, both in a centralized and distributed context, as long as they are applied to a network having a single communication protocol. The problem becomes more complex in the multi-protocol case, where there is a possibility of encapsulation of some network protocols into others, therefore inducing nested tunnels. The classic algorithms cited above no longer work in this case because they cannot manage the protocol encapsulations and the corresponding protocol stacks. In this work, we propose a highly parallelizable algorithm that takes into account protocol encapsulations as well as protocol conversions in order to compute shortest paths in a multi-protocol network. To achieve this computation efficiently, we study the transitive closure between subpaths (i.e., the concatenation of two subpaths to obtain a longer one) in the case where each subpath induces a protocol stack, and thus tunnels. Leveraging on Software-Defined Networks with a controller having a highly parallel architecture enables us to compute the routing tables of all nodes in a very efficient way. Experimentation results on both random and realistic topologies show that our algorithm outperforms the previous solutions proposed in the literature.</p>
Data from: Social networks reveal sex- and age-patterned social structure in Butler's Gartersnakes
<p>Sex- and age-based social structures have been well-documented in animals with visible aggregations. However, very little is known about the social structures of snakes. This is most likely because snakes are often considered non-social animals and are particularly difficult to observe in the wild. Here, we show that wild Butler's Gartersnakes have an age and sex assorted social structure similar to more commonly studied social animals. To demonstrate this, we use data from a 12-year capture-mark-recapture study to identify social interactions using social network analyses. We find that the social structures of Butler's Gartersnakes comprise sex- and age-assorted intra-species communities with older females often central and age segregation partially due to patterns of study site use. In addition, we find that females tended to increase in sociability as they aged while the opposite occurred in males. We also present evidence that social interaction may provide fitness benefits, where snakes that were part of a social network were more likely to have improved body condition. We demonstrate that conventional capture data can reveal valuable information on social structures in cryptic species. This is particularly valuable as research has consistently demonstrated that understanding social structure is important for conservation efforts. Additionally, research on the social patterns of animals without obvious social groups provides valuable insight into the evolution of group living.</p>
Dataset for paper 'McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks'
<p>The .zip file includes four datasets for testing the performance of McAN (doi: https://doi.org/10.1093/bib/bbad174).</p>
Optimal mechanical interactions direct multicellular network formation on elastic substrates
<p>Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how intercellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue engineering. Here, by combining an agent-based model for contractile cells on elastic substrates with endothelial cell culture experiments, we show that substrate deformation–mediated mechanical interactions between cells can cluster and align them into branched networks. Motivated by the structure and function of vasculogenic networks, we predict how measures of network connectivity like percolation probability and fractal dimension as well as local morphological features including junctions, branches, and rings depend on cell contractility and density and on substrate elastic properties including stiffness and compressibility. We predict and confirm with experiments that cell network formation is substrate stiffness dependent, being optimal at intermediate stiffness. We also show the agreement between experimental data and predicted cell cluster types by mapping a combined phase diagram in cell density substrate stiffness. Overall, we show that long-range, mechanical interactions provide an optimal and general strategy for multicellular self-organization, leading to more robust and efficient realizations of space-spanning networks than through just local intercellular interactions.</p>
HDAC6 protein-protein interaction network in CNS
<p>HDAC6 stands out as a distinctive member within the histone deacetylase family due to its predominant presence in the cytosol, facilitating its interaction with a wide array of non-histone proteins. Its dysregulation has been linked to various outcomes, encompassing diverse cancer types, immune-related disorders, and neurological conditions, including Alzheimer's, Parkinson's, ALS, Huntington's, Charcot-Marie-Tooth disease, and Rett syndrome.</p><p>The current network represents the known HDAC6 interactions in the central nervous system (CNS).</p><p>The latest version of the current network is available in WikiPathways under accession number WP5426.</p>
Theory and implementation of inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Theory and implementation of inelastic Constitutive Artificial Neural Networks.</em></p> <p>arXiv: <a href="https://doi.org/10.48550/arXiv.2311.06380">https://doi.org/10.48550/arXiv.2311.06380</a></p> <p>Computer Methods in Applied Mechanics and Engineering: <a href="https://doi.org/10.1016/j.cma.2024.117063">https://doi.org/10.1016/j.cma.2024.117063</a></p> <p> </p> <p><strong>01_Example01: </strong> Artificially generated data</p> <p>This example investigates whether the iCANN is able to discover a model for the data generated by a continuum mechanical model.</p> <p> </p> <p><strong>02_Example02:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p> <p> </p> <p><strong>03_Example03: </strong>Discovering a model for passive skeletal muscle subjected to relaxation</p> <p>In this example, we investigate whether the iCANN is able to discover a model for the material behavior of passive skeletal muscles. A total of five independent experiments are carried out in which the maximum applied compression stretch and the stretch rate are varied. In addition, the learning performance of the iCANN is investigated. Training is first carried out in each of the five experiments and then in each of four of the five experiments.</p> <p>The experimental data are taken from the literature:</p> <p>Van Loocke, M., Lyons, C. G., & Simms, C. K. (2008). Viscoelastic properties of passive skeletal muscle in compression: stress-relaxation behaviour and constitutive modelling. <em>Journal of biomechanics</em>, <em>41</em>(7), 1555-1566.</p> <p><a href="https://doi.org/10.1016/j.jbiomech.2008.02.007">https://doi.org/10.1016/j.jbiomech.2008.02.007</a></p> <p> </p> <p><strong>python_requirements.txt: </strong>File containing a list of installed Python modules used to implement the iCANN</p>
(Dataset) Similarity in Consumption Patterns among Peasant Communities in Roman Central Hispania through Network Science
<p>Dataset and R script for the Brainerd-Robinson similarity analyses and dataset of the paper Similarity in Consumption Patterns among Peasant Communities in Roman Central Hispania through Network Science. Journal of Computer Applications in Archaeology. </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.