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1,721 results for “network data”
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)
<p>Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p>SVP1 data missing for June 17-18 (2021) and July 5-7 (2021).</p> <p><br> Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) June 9-11 - 2021</p> <p>AIS recorded data not available after June 11, 2021</p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
Data and R files for the analysis of the innovative capacity and the network position of national manufacturing industries in world production
<p>Data and R files for the reproducibility of the results obtained in Kim and Ozaygen, Analysis of the innovative capacity and the network position of national manufacturing industries in world production.</p> <p>It also includes an R/Shiny application which runs at <a href="https://awekim.shinyapps.io/Manuf_shiny_R/">https://awekim.shinyapps.io/Manuf_shiny_R/ </a></p>
The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China
<p>The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.</p>
Training data for benchtop NMR and UV/vis spectroscopy for Artificial Neural Networks
<p>Data set of low-field NMR spectra and UV/vis spectra for the synthesis of mesalazine intermediates, which were used as training or validation data for data processing with artificial neural networks development</p> <p><strong>Low-field NMR spectra for the nitration step:</strong></p> <p>The pure component spectrum of 2ClBA, 3N-2ClBA, and 5N-2ClBA are marked as NMR_pure_spectrum. The concentration levels for 2ClBA, 3N-2ClBA and 5N-2ClBA are in row 1, 2, and 3, respectively.</p> <p>The data sets marked as NMR_ represents low-field NMR-spectra recorded. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively.</p> <p><strong>Datafusion data sets for the hydrolysis and nitration step</strong></p> <p>The NMR data are either recorded or simulated from the pure NMR spectrum of each individual component. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are either assigned with UHPLC measurements or calculated from the prepared solutions.</p> <p>The NMR spectra are depicted in datafusion_NMR_training. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively.</p> <p>The UV/vis spectra are depicted in datafusion_UVvis_training. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are in column 1, 2, 3, 4, and 5, respectively.</p> <p><strong>Process data</strong></p> <p>The NMR spectra for the stability run and the run with dynamic changes are depicted in process_NMR_. The first column is the time stamp.</p> <p>The UV/vis spectra for the stability run and the run with dynamic changes are depicted in process_UV_. The first column is the time stamp.</p>
Convolutional neural network and data used for applied soundscape classification with Soundscapes 2 Landscapes (S2L)
<p>This repository documents the ABGQI-CNN manuscript (DOI: <a href="https://doi.org/10.1016/j.ecolind.2022.108831">https://doi.org/10.1016/j.ecolind.2022.108831</a>). It contains supplementary materials, data used to train a soundscape classification convolutional neural network (CNN), and data to generate manuscript results. The accompanying code can be found at <a href="https://doi.org/10.5281/zenodo.6038460">https://doi.org/10.5281/zenodo.6038459</a>. Files include:</p> <ul> <li><strong>ABGQI-CNN.tar: </strong>saved CNN model weights for the 5-class soundscape classifier using a MobileNetV2 architecture pre-trained with bird vocalization data.</li> <li><strong>ABGQI_mel_spectrograms.tar</strong>: spectrograms used for fine-tuning the pre-trained CNN, above, with training, validation, and testing data splits.</li> <li><strong>freesound_licensing.csv</strong>: file names and license information related to Freesound auxiliary files.</li> <li><strong>RavenLite_Training_Data_Collection.pdf</strong>: a manual for RavenLite ROI annotation.</li> <li><strong>S2L_site_geog-env_data.csv</strong>: environmental and geographic data (sans GPS) related to site locations in S2L project 2017-2020.</li> <li><strong>site_avg_ABGQIU_fscore_075_daytime.csv</strong>: the average site rate of soundscape components for 5 a.m. to 8 p.m.</li> <li><strong>site_by_hour_ABGQIU_fscore_075.csv</strong>: the average hourly site rate of soundscape components</li> <li><strong>site_classifications_beta075.tar</strong>: a directory containing a CSV for every site with threshold optimized classifications for each 2-s Mel spectrogram</li> <li><strong>site_prediction_probabilies.tar</strong>: a directory containing a CSV for every site with ABGQI-CNN probabilities for each 2-s Mel spectrogram</li> <li><strong>Supplementary_Materials.pdf</strong>: includes additional material and analyses related to the accompanying manuscript. </li> </ul> <p>Contact Colin Quinn at cq73@nau.edu for questions related to this repository or if you have an interest in the original wav recordings. Please be aware that underlying software, specifically for the CNN implementation, may not continue stability as python libraries are updated.</p>
Code and data from: Familiarity, dominance, sex and season shape common waxbill social networks.
<p><span>In gregarious animals, social network positions of individuals may influence their life-history and fitness. Although </span><span>association patterns </span><span>and the position of individuals </span><span>in social networks </span><span>can be shaped by phenotypic differences and by past interactions, few studies have quantified their relative importance. We evaluated how phenotypic differences and familiarity influence social preferences and the position of individuals within the social network. We monitored wild-caught common waxbills (<em>Estrilda astrild</em>) with radio-frequency identifiers in a large mesocosm during the non-breeding and breeding seasons of two consecutive years. We found that social networks were similar, and that the centrality of individuals was repeatable, across seasons and years, indicating a stable social phenotype. Nonetheless, there were seasonal changes in social structure: waxbills associated more strongly with opposite-sex individuals in breeding seasons, while in non-breeding seasons they instead assorted according to similarities in social dominance. We also observed stronger assortment between birds that were introduced to the mesocosm at the same time, indicating long-lasting bonds among familiar individuals. Waxbills that had been introduced to the mesocosm more recently occupied more central network positions, especially during breeding seasons, perhaps indicating that these birds </span><span>had</span><span> less </span><span>socially-differentiated associations with</span><span> flock members. Finally, individual differences in color ornamentation and behavioral assays of personality, inhibitory control and stress were not related to network centrality or association patterns</span><span>.</span><span> Together, these results suggest that, in gregarious species like the common waxbill, social networks may be more strongly shaped by long-lasting associations with familiar individuals than by phenotypic differences among group members.</span></p>
4D-Var data assimilation experiment of the Lorenz 96 model using an adjoint model of a neural network surrogate model
<p>These data are the output of the 4D-Var data assimilation experiment of the Lorenz96 model using an adjoint model of a neural network surrogate model.<br> The details are described in Nishizawa (2022).<br> </p>
Strateole-2 data set associated to the publication "A seismic network in the stratosphere"
<p>NetCDF files of the pressure and temperature data of TSEN sensors, and GPS coordinates, on board EUROS gondolas of Strateole-2 project (stratospheric balloons deployed during fall 2021).</p> <p>One file per gondola, associated to the 4 balloons detecting the Flores quake (2021/12/14 3:20:35.8 GMT) and to a single balloon detecting the Northern Peru quake (2021/11/28 10:52:25.8 GMT).</p> <p>These data cover one hour before and 2 hours after the quake. The rest of the Strateole-2 data will be released by the project. This SUbset is associated to the publication "A seismic network in the stratosphere.</p>
Data sets used in "Neural network processing of holographic images"
<p>Included are the training, validation, and testing data sets for synthetic holograms (netCDF), the HOLODEC data set containing the RF07 examples (netCDF), and the two splits of manually labeled HOLODEC image tiles (numpy arrays). The source code for using the data sets can be found at https://github.com/NCAR/holodec-ml </p>
Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows:</p> <p>In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.</p>
Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling
<p>This dataset include data from Mental Health network of Gipuzkoa (Spain). It is included information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript: "Almeda, N., Garcia-Alonso, C. R., Gutierrez-Colosia, M. R., Salinas-Perez, J. A., Iruin-Sanz, A., & Salvador-Carulla, L. (2022). Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem. PLoS ONE, 17(1 January), 1–16. https://doi.org/10.1371/journal.pone.0261621". This manuscript has been published in Plos One journal.</p> <p>This research focused on developing a formal causal model based on Bayesian network prototypes which were designed by formalizing expert knowledge (by using Expertbased Cooperative Analysis) and resulting in Direct Acyclic Graphs. The best Bayesian networks and their corresponding regression models were used to estimate the statistical ranges or confidence intervals for the dependent variable (potential effect, consequence, or output) given the independent variable values. These ranges, adjusted to delimited statistical distributions (triangular, trapezoidal and gamma), were managed by a Monte Carlo simulation engine for intervention assessment. A computer-based Decision Support System (DSS) was used to assess the status of ecosystem performance: RTE, statistical stability and entropy.</p> <p>Main results of the analyses pointed out that by combining causal reasoning and statistical methods, decision makers can obtain a deep view of both pre-implementing and post-implementing situations. Knowing the causal levers, it is possible to act directly to the causes in order to potentially produce de appropriate results considering the uncertainty: to provide a more balanced and integrated MH care provision in the community. In this particular case, an improvement in the outpatient workforce increases both ecosystem performance (RTE) and stability and slightly decreases entropy.</p>
OMEN data set: Tokheim pancancer data set with genome-wide interaction network
<p>OMEN is a Network-based Driver Gene Identification method that exploits Mutual Exclusivity.<br> This repository stores the data used in a pancancer experiment showcasing this method.</p> <p>It consists of</p> <p>- [tokheim_pancancer_somatic_CADD.pl] A file containing CADD probabilities for gene-patient pairs (derived from the pancancer data set in Tokheim, Collin J., et al. "Evaluating the evaluation of cancer driver genes." <em>Proceedings of the National Academy of Sciences</em> 113.50 (2016): 14330-14335.) formatted to be used by OMEN.<br> - [tokheim_pancancer_somatic_coverage_ranks.pl] A file containing gene coverage data (derived from the CADD data) formatted to be used by OMEN.<br> - [network.pl] A file containing a genome-wide interaction network consisting of high quality metabolic interactions from Recon X and literature curated interactions from Intact. Recon X and Intact data was acquired from Pathway Commons version 8.<br> The resulting network covers 7901 samples, and contains 15.694 nodes and 178.051 edges.</p>
Echolocation clicks and anthropogenic detections with neural network labels in Hawaiian Island HARP data from Kona, Kaua`i, and Pearl and Hermes Reef
<p><span>This dataset consists of echolocation clicks and detections of anthropogenic signals at three sites in the Hawaiian Islands Archipelago. These sites are </span><span>Hawaii/Hawaii_K, </span><span>Kauai/KA, and </span><span>Pearl and Hermes Reef/PHR. </span><span>Echolocation clicks were grouped into 5 minute bins, for which summary data is provided. Files are in .mat format that can be read using any desired coding language using a netcdf reading script. Files are separated by site, deployment, and neural network class (i.e. sitedeployment_cbins_class or site_deployment_cbins_class). Manual labels are provided.</span></p>
Data from: Flowering overlap and floral trait similarity help explain the structure of pollination network
<p><span>Co-flowering communities are usually characterized by high plant generalization but knowledge of the underlying factors leading to high levels of generalization and pollinator sharing, and how these may contribute to network structure is still limited. </span>Flowering phenology and floral trait similarity are considered among the most important factors determining plant generalization and pollinator sharing. However, these have been evaluated independently even though they can act in concert with each other. Moreover, the importance of flowering phenology and floral similarity, via their effects on plant generalization, in the structure of plant–pollinator networks have been scarcely studied. Here, we aim to evaluate the effect of flowering phenology and floral similarity in mediating the degree of pollinator sharing and plant generalization in two coastal communities and uncover their importance as drivers of plant–pollinator network structure.</p> <p>We recorded flower production per species, as well as the identity and frequency of floral visitors along the entire flowering season. We estimated the degree of flowering overlap, the degree of floral similarity (using floral traits associated with size and color), and the degree of pollinator sharing among plant species within both communities.</p> <p>Structural equation models (SEM) showed a positive effect of flowering overlap on pollinator sharing and plant generalization. Pollinator sharing and plant generalization positively affected network nestedness. Furthermore, SEM showed a direct positive effect of flowering overlap on network modularity. The SEM analyses also revealed a significant interaction effect of floral similarity and flowering overlap on pollinator sharing, with consequences for network nestedness in one community.</p> <p><span>Our results highlight the importance of integrating multiple axes of differentiation such as flowering phenology and floral similarity into our understanding of the drivers of plant–pollinator network structure.</span></p>
Data from: Building communities of teaching practice and data-driven open education resources with NEON faculty mentoring networks
<p>With the growing availability and accessibility of big data in ecology, we face an urgent need to train the next generation of scientists in data science practices and tools. One of the biggest barriers for implementing a data-driven curriculum in undergraduate classrooms is the lack of training and support for educators to develop their own skills and time to incorporate these principles into existing courses or develop new ones. Alongside the research goals of the National Ecological Observatory Network (NEON), providing education and training are key components for building a community of scientists and users equipped to utilize large-scale ecological and environmental data. To address this need, the NEON Data Education Fellows program formed as a collaborative Faculty Mentoring Network (FMN) between scientists from NEON and university faculty interested in using NEON data and resources in their ecology classrooms. Like other FMNs, this group has two main goals: 1) to provide tools, resources, and support for faculty interested in developing data-driven curriculum, and (2) to make teaching materials that have been implemented and tested in the classroom available as open educational resources for other educators. We hosted this program using an open education and collaboration platform from the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project. Here, we share lessons learned from facilitating five FMN cohorts and emphasize the successes, pitfalls, and opportunities for developing open education resources through community-driven collaborations.</p>
Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
<p><strong>Aim</strong>: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analysing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts.</p> <p><strong>Location</strong>: Global.</p> <p><strong>Methods</strong>: We collated telemetry data from 1,235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections.</p> <p><strong>Results</strong>: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links.</p> <p><strong>Main conclusions</strong>: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness, but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed towards areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.</p>
Research data: "Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe"
<p>This data set belongs to the paper Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe in the International Journal of Production Economics (DOI). The BatPac model, as well as, the model for the economic assessment of the recycling route are not included. The needed data can be found in the file (name). Further, the BatPaC model can be gather from the website of Argonne National Laboratory and the assessment tool for the recycling routes via this DOI: 10.5281/zenodo.6500946.</p> <p> </p> <p>This work is part of the research project Recycling 4.0 (EFRE | ZW 6-85018080), which is funded by the European Regional Development Fund and managed by the development bank for the German federal state of Lower Saxony (NBank).</p>
Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"
<p>This dataset contains the untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community". The dataset has also been submitted to MetaboLights repository with ID "MTBLS3535". Please refer to the MetaboLights repository for the most up-to-date datasets. </p> <p>Publication abstract:</p> <p>A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM<sup>12</sup>) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM<sup>12</sup> interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, <em>Enterococcus faecalis</em> KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM<sup>12</sup> interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.</p>
Data for "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks"
<p>These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks" (DOI 10.1039/D2SM00452F). Three of the directories contains subdirectories with `GSD` files dumped from HOOMD. The other contains pretrained RNN models as TorchScript binaries exported from PyTorch.</p>
Data supporting: Drivers of individual-based, antagonistic interaction networks during plant range expansion
<p><span>1. Range expansion in plant populations, especially at the colonization front, can be either limited by disproportionately large effects of antagonistic interactions or facilitated by their release. How the strength of antagonistic interactions changes along successional gradients during range expansion is still poorly documented, especially when diverse assemblages of plant antagonists (rodents, invertebrates, and birds) combine within interaction networks.</span></p> <p><span>2. We study the changes in individual-based, predispersal seed-pulp predator networks along a colonization gradient in a rapidly-expanding <em>Juniperus phoenicea</em> population in Doñana National Park (SW Spain). Additionally, we analysed the role of individual plant traits and neighbourhood attributes in network configuration by using Exponential Random Graph Models.</span></p> <p><span>3. Seven seed-pulp consumer animal species varied significantly in their frequency of interaction and prevalence. While invertebrate species were well established in old and intermediately mature stands, greenfinch (<em>Chloris chloris</em>) was dominant at the colonization front. Variable species roles and spread of interactions among individual plants generated changes in the configuration of interactions during plant expansion.</span></p> <p><span>4. Individual plant traits strongly determined the topology of these networks, although with differences between stands. Increasing individual crop size and seeds per cone increased the interaction odds of individual plants, while seed viability showed the opposite effect. The network topology at the colonization front appeared less driven by individual traits, possibly because of the short interaction history of this recently established area. The disproportionately large effect of <em>C. chloris</em> in these recently established stands, potentially resulted in large seed losses during range expansion.</span></p> <p><span>5.</span><em><span> Synthesis</span></em><span>. Turnover of antagonistic interactions, characterized the colonization front, resulting in more heterogeneous interaction strengths among individual plants. We found no evidence for a complete or sizeable antagonistic release of <em>J. phoenicea</em> at the colonization front promoting this rapid expansion. It becomes necessary to explore interactions with seed dispersers to understand how antagonistic and mutualistic plant-animal interactions balance during range expansion. Our study highlights the importance of an individual-based approach in understanding how interactions are structured and driven in natural changing landscapes.</span></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.