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619 results for “configuration”
Fig. 4 in Evidence for a Sauropod-Like Metacarpal Configuration in Ankylosaurian Dinosaurs
Fig. 4. Proximal views of the metacarpus in the Dinosauria, showing that a semicircular configuration is present only in the Thyreophora and basal Sauropoda. Sources of drawings are as follows: Saichania, Maryańska (1977); Stegosaurus, Senter (2010); Triceratops, Fujiwara (2009); Camptosaurus, Carpenter and Wilson (2008); Herrerasaurus, Massospondylus, Omeisaurus, Brachiosaurus, and Apatosaurus, Bonnan (2003); Dilophosaurus, modified from photo by author. Roman numerals refer to digit number.
Fig. 3 in Evidence for a Sauropod-Like Metacarpal Configuration in Ankylosaurian Dinosaurs
Fig. 3. Left manual skeleton of the ankylosaur Peloroplites cedrimontanus Carpenter, Bartlett, Bird, and Barrett, 2008 from Cedar Mountain Formation, Utah, USA (CEUM 12187–12193, 12218–12223); articulated correctly and incorrectly. A. Metacarpals in pollucal view, correctly articulated with phalanges: I (A1), II (A2), and III (A3). B. Metacarpals in proximal view, correctly articulated without (B1) and with (B2) available phalanges. C–F. Correctly (C1–F1) and incorrectly (C2–F2) articulated metacarpals shown in four oblique views with (C1–F1) and without (C2–F2) available phalanges. C. Craniodorsal view, centered on digit II. D. Craniodorsal view, centered on digit III. E. Laterodorsal view, centered between digits III and IV. F. Caudodorsal view, centered between digits IV and V. In both configurations the metacarpals are arranged in a tight arc, but they are vertical and parallel to each other in the correct configuration, whereas they are slanted and distally divergent in the incorrect configuration. Roman numerals refer to digit number.
Fig. 1 in Evidence for a Sauropod-Like Metacarpal Configuration in Ankylosaurian Dinosaurs
Fig. 1. The manus in mounted skeletons of ankylosaurs, showing metacarpals incorrectly configured in a shallow arc with their shafts slanted and their distal ends divergent. A. Gastonia burgei Kirkland, 1998 from Cedar Mountain Formation, Utah, USA; College of Eastern Utah Prehistoric Museum, Price, Utah, in oblique dorsolateral view. B. Edmontonia rugosidens Gilmore, 1930 from Dinosaur Park Formation, Alberta, Canada; American Museum of Natural History, New York City, New York, USA, in oblique dorsolateral (B1) and medial (B2) views.
Ultrafast photoresponse of vertically oriented TMD films probed in a vertical electrode configuration on Si chips
<p>This dataset contains the measurement data for figures published in the journal article: </p> <p> Ultrafast photoresponse of vertically oriented TMD films probed in a vertical electrode configuration on Si chips (https://doi.org/10.1039/D2NA00313A)</p> <p>by Topias Järvinen, Seyed-Hossein Hosseini Shokouh, Sami Sainio, Olli Pitkänen and Krisztian Kordas</p>
Can green hydrogen drive economic transformation in Saudi Arabia? - An input-output analysis of different Power-to-X configurations. Supplementary Data
<p>Supplementary material for peer review</p> <ul> <li>Modelling Data (input & results)</li> <li>Literature Review</li> </ul>
Mesh and configuration files to perform coupled heat+fluid simulations on a realistic human eyeball geometry with Feel++
<h1>Run the simulation</h1> <h2>With slurm</h2> <p>Set up position and desired mesh in the `run.slurm` file. Then, submit the job with the following command:</p> <p><code>sbatch run.slurm</code><br><br></p> <h2>Without slurm</h2> <p>Run by hand the command of the <code>run.slurm</code> file.<br><code>POSITION=prone # prone supine standing</code><br><code>SOLVER_TYPE=simple # simple lsc</code><br><code>MESH_INDEX=M4 # M1 M2 M3 M4 M5</code></p> <p><code>mpirun -np 128 feelpp_toolbox_heatfluid \</code><br><code> --config-files eye-${POSITION}.cfg pc_${SOLVER_TYPE}.cfg \</code><br><code> --heat-fluid.json.patch='{ "op": "replace", "path": "/Meshes/heatfluid/Import/filename", "value": "$cfgdir/mesh/Mr/'${MESH_INDEX}'/Eye_Mesh3D_p$np.json" }' \</code><br><code> --heat-fluid.scalability-save=1 --heat-fluid.heat.scalability-save=1 --heat-fluid.fluid.scalability-save=1</code></p> <h2>Available meshes</h2> <p>The meshes are available and are already partitioned for parallel computing:</p> <p><code>M0 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M1 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M2 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M3 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M4 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M5 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M6 : 128, 256, 384, 512, 640, 768</code></p>
data source for paper "Unity of granular configuration of soil"
<ol> <li>'Table S1' presents soil data sources from China, around the world, and from lunar samples, including their abbreviations, soil textures, locations, and specimen numbers.</li> <li>'GSD data S2' contains grain size distribution data for soils belonging to four models: unimodal, bimodal, trimodal and multimodal.</li> <li>'Matlab code for simulating soil grain size data S3' is designed to simulate soil particles from the source area.</li> </ol>
Full electronic tables for 'Spectrum and energy levels of the high-lying singly excited configurations of Nd III'
<p>Full electronic versions of table extracts of the accepted version of the preprint at <a href="https://doi.org/10.48550/arXiv.2408.07830">https://doi.org/10.48550/arXiv.2408.07830</a></p>
Data from: Restoring marine ecosystems: spatial reef configuration triggers taxon-specific responses among early colonizers
<p>1. The longstanding debate in conservation biology on the importance of single large or several small (SLOSS) habitats for preserving biodiversity remains highly relevant, given the ongoing degradation and loss of natural habitats worldwide. Restoration efforts are often constrained by limited resources, and insights from SLOSS studies therefore have important implications if restoration efforts can be optimized by manipulating the spatial configuration of restored habitats. Yet, the relevance of SLOSS for habitat restoration remains largely unexplored.</p> <p>2. Here, we report the effects of spatial reef configuration on early colonization of marine organisms after restoring boulder reef habitats. Reefs were restored in single large (SL) and several small (SS) designs in the western Baltic Sea, where century-long boulder extraction has severely degraded large reef areas and likely exacerbated regional declines in commercially important gadoids (<i>Gadidae spp.</i>). We sampled the field sites using remote underwater video systems in a before-after control-impact (BACI) design and obtained probabilistic inferences on restoration and SLOSS effects from Bayesian hierarchical models.</p> <p>3. Probabilities of a positive restoration effect were high (>95%) for gadoids, labrids and demersal gobies, moderate (60-75%) for species richness and sand gobies, and low (<5%) for flatfish abundance. Notably, gadoid abundance increased 60-fold and 129-fold on average at SL and SS, respectively. The species composition at restored reefs deviated from control sites, mainly driven by large-bodied piscivores.</p> <p>4. Spatial reef configuration had the strongest effect on small-bodied mesopredators, including gobies, which were more abundant and driving a distinct species assemblage at SS. In addition to providing suitable conditions for reef species, results suggest that SS can also benefit soft-bottom taxa, possibly through a dispersed predator-mediated effect relative to SL. </p> <p><i>5. Synthesis and applications.</i> This study demonstrates that boulder reef restoration can strongly promote the abundance of exploited gadoids and is therefore a promising management tool to support top-down controls by predatory fishes in degraded marine systems. The higher abundance of mesopredators at SS reefs suggests that SLOSS could have long-term implications for trophic structure and resilience of restored habitats, and should therefore become an important facet within restoration strategies. </p>
Arctic cubed sphere configuration of MITgcm with 2-km resolution
<p>This repository includes a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm,<a href="https://doi.org/10.1029/96JC02775">Marshall et al., 1997</a>; <a href="http://mitgcm.org/public/docs.html">MITgcm Group, 2017</a>) with a horizontal resolution of 2 km, which is described in <a href="https://doi.org/10.5194/tc-14-93-2020">Hutter & Losch (2020)</a>. It is based on a regional Arctic configuration (<a href="https://doi.org/10.1175/JPO-D-11-040.1">Nguyen et al., 2012</a>) of the MITgcm, which represents the Northern face of a global cubed sphere configuration. The number of vertical layers is reduced to 16, with the first 5 layers covering the uppermost 120 m to decrease the computational cost associated with the ocean model component. The Refined Topography dataset 2 (RTopo-2) (<a href="https://doi.org/10.1594/PANGAEA.856844">Schaffer and Timmermann, 2016</a>) is used as bathymetry for the entire model domain. The lateral boundary conditions are taken from the globally optimized ECCO-2 simulations (Menemenlis et al., 2008). The configuration is designed to use the 3-hourly Japanese 55-year Reanalysis (JRA-55, <a href="https://doi.org/10.2151/jmsj.2015-001">Kobayashi et al., 2015</a>) with a spatial resolution of 0.5625° for surface boundary conditions. The ocean temperature and salinity are initialized on 1 January, 1992, from the World Ocean Atlas 2005 (Locarnini et al., 2006; Antonov et al., 2006). The initial conditions for sea ice are taken from the Polar Science Center (<a href="http://doi.org/10.1029/2001JC001041">Zhang et al., 2003</a>). Ocean and sea ice parameterizations and parameters are directly taken from <a href="https://doi.org/10.1029/2010JC006573">Nguyen et al. (2011)</a>, with the ice strength P=2.264×104Nm−2. The configuration uses the classical discrimination of two ice classes: thin and thick ice (<a href="https://doi.org/10.1175/1520-0485(1979)009%3C0815:ADTSIM%3E2.0.CO;2">Hibler, 1979</a>). The momentum equations are solved by an iterative method and line successive relaxation (LSR) of the linearized equations following <a href="https://doi.org/10.1029/96JC03744">Zhang and Hibler (1997)</a>. In each time step (<span class="math-tex">\(\Delta\)</span>t=120 s), 10 nonlinear steps are made and the linear problem is iterated until an accuracy of 10e−5 is reached or 500 iterations are performed. With this configuration, simulations were run from 1 January 1992 to 31 December 2012. We also provide pickup files to restart the simulation in 2012.</p> <p>To reference this configuration please use the citation of this repository and reference to the paper describing the configuration:</p> <p>Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, <a href="https://doi.org/10.5194/tc-14-93-2020">https://doi.org/10.5194/tc-14-93-2020</a>, 2020.</p>
Coordinates of CHP and non-CHP configurations in regular polygons
<p>We report the coordinates of the CHP configurations in several regular polygons, for different numbers of shells.</p>
Coordinates of the packing configurations of arXiv:2212.12287 [cs.CG]
<p>We provide the cartesian coordinates of the packing configurations in regular polygons obtained in https://doi.org/10.48550/arXiv.2212.12287</p> <table summary="Additional metadata"> <tbody> <tr> <td> </td> <td> </td> </tr> </tbody> </table>
Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)
<p>This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen.</p> <p>In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data.</p> <p>Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from <strong>one </strong>CPS component (i.e., production station) at <strong>one </strong>point in time.</p> <p>The data set contains the following files</p> <ul> <li>low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators <ul> <li>*.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory</li> </ul> </li> <li>camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution</li> <li>camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution</li> </ul> <p>Check the following publications to learn more about our research using the model factory:</p> <p>Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In <em>Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18</em> (pp. 95-107). Springer International Publishing.</p> <p>Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In <em>2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)</em> (pp. 20-26). IEEE.</p> <p>Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. <em>Journal of Manufacturing Systems</em>, <em>63</em>, 575-592.</p>
Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems (Supplementary Material)
<p>This repository provides supplementary material to the ICSE 2023 paper "Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems". We provide the following material:</p> <p>- The experimental setup, including the performance and measurement scripts.</p> <p>- (Aggregated) measurement data and configurations used in our analysis as well as the raw code coverage measurements.</p> <p>- An interactive dashboard to reproduce and reenact our analyses/findings, re-create all visualizations used in the original paper and those omitted due to space limitations.<br> <br> The repository is structured as follows:<br> <br> - accepted_paper.pdf: Camera-ready version of the original paper for reference.</p> <p>- coverages_raw.tar.gz: Raw coverage reports as compressed CSV files (uncrompressed: ~60 GB)</p> <p>- artifacts_excluding_raw_coverage.zip: aggregated measurement data, interactive dashboard, and experimental setup</p> <p>- README.md: A detailed documentation of all the material provided.</p>
H2020 Enodise: Experimental dataset configuration B2 ECL
<p>This dataset considers experimental aerodynamic and acoustic data for configuration B2 as defined in the H2020 ENODISE project (https://www.vki.ac.be/index.php/about-enodise). Wing static pressure, propellers' loads, and far-field sound measurements were performed in an open-jet anechoic facility with a Distributed Electric Propulsion (DEP) configuration model which includes a wing and two side-by-side propellers.</p>
Datasets and configuration files for EmbDI: Embeddings for Data Integration
<p>## License</p> <p>```<br> Copyright 2020 Riccardo CAPPUZZO</p> <p> Licensed under the Apache License, Version 2.0 (the "License");<br> you may not use this file except in compliance with the License.<br> You may obtain a copy of the License at</p> <p> http://www.apache.org/licenses/LICENSE-2.0</p> <p> Unless required by applicable law or agreed to in writing, software<br> distributed under the License is distributed on an "AS IS" BASIS,<br> WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<br> See the License for the specific language governing permissions and<br> limitations under the License.<br> ```</p> <p>## EmbDI datasets</p> <p>The datasets contained in this directory were used while working with [EmbDI](https://gitlab.eurecom.fr/cappuzzo/embdi) on the relevant paper. Please refer to the full repository for more info.</p> <p>What is provided here was sourced mostly from [The Magellan Data Repository](https://sites.google.com/site/anhaidgroup/useful-stuff/data#TOC-The-Corleone-Data-Sets). For each dataset, three tables are provided: table-A and table-B are taken from the original repository and slightly modified (lower casing, spaces were replaced by `_`, some special characters were removed), while the third table is the concatenation of tables A and B.</p> <p>## Edgelists</p> <p>Edgelists are the data structures used by EmbDI. They are generated starting from each concatenated dataset and are then fed to the algorithm.</p> <p>## EQ tests</p> <p>The `EQ tests` folder contains all the tests used to perform the Embeddings Quality evaluation in the paper.</p> <p>The additional resources include:</p> <p>* (partially preprocessed) base datasets.</p> <p>* Their Entity Resolution and Schema Matching versions.</p> <p>* Edgelists for both ER and SM versions.</p> <p>* Ground truth files for ER and SM tasks.</p> <p>* Test directories for the EQ task.</p> <p>* Copies of the configuration files provided in this repository.</p> <p> </p> <p>Configuration files, info files and ER match files were left in this repository in `pipeline/config_files/default`,</p> <p>`pipeline/info` and `pipeline/matches/default`.</p> <p><br> </p>
BeMAGIC_Magnetoelectric measurements using solid configurations (transistor, condenser-like)
<p><strong>BeMAGIC_ITN_(GA861145)_Magnetoelectric measeurements using solid configurations (transistor, condenser-like). Results from ICN2, GTECM, VOXALYTIC and AALTO.</strong></p>
Carbon configurations dataset generated in "A systematic approach to generating accurate neural network potentials: the case of carbon"
<p>This dataset contains 60133 configurations of Carbon as generated in the paper "A systematic approach to generating accurate neural network potentials: the case of carbon". The configurations represent crystal structures containing only Carbon atoms, ranging from 16 to 200 atoms in the unit cell, with energy and forces calculated through density functional theory. Please refer to the original paper for more details about the generation procedure and simulation parameters.</p> <p>The dataset consists of a compressed archive containing a single .xyz file with all configurations, with lattice and energy information contained in the comment line, and one line per atom with positions and forces. Lengths are in Angstroms and energies in eV.</p>
Data for Demonstration of a Quantum Switch in a Sagnac Configuration
<p># Data accompanying the paper"Demonstration of a Quantum Switch in a Sagnac Configuration" (arXiv:2211.12540v1).</p> <p>"datastore_H.h5" and "datastore_P.h5" contain python/pandas dataframes holding coincidences counts aquired during the measurement.</p> <p>"tomos.h5" cointains a python/pandas datafraframe holding the polarization tomography measurements on the individual polarization gadgets.</p> <p>"read.py" gives an example of how to open the files.</p> <p>The DataFrames "datastore_H.h5" and "datastore_P.h5" contain the following columns:</p> <p>- **i, j**: indices of the implemented unitaries, as defined in the paper.<br> - **run**: index of repetition of the whole measurement.<br> - **cc_h_com**: integrated coincidences aquired in the H-port of the polarization tomography stage put after the commutator output port of the sagnac interferometer.<br> - **cc_v_com**: integrated coincidences aquired in the V-port of the polarization tomography stage put after the commutator output port of the sagnac interferometer.<br> - **cc_h_acom**: integrated coincidences aquired in the H-port of the polarization tomography stage put after the anticommutator output port of the sagnac interferometer.<br> - **cc_v_acom**: integrated coincidences aquired in the V-port of the polarization tomography stage put after the anticommutator output port of the sagnac interferometer.<br> - **cc_h_com_dark**: integrated dark coincidences aquired in this port.<br> - **cc_v_com_dark**: integrated dark coincidences aquired in this port.<br> - **cc_h_acom_dark**: integrated dark coincidences aquired in this port.<br> - **cc_v_acom_dark**: integrated dark coincidences aquired in this port.<br> - **triggers_com**: integrated number of trigger events leading to coincidences in the commutator port.<br> - **triggers_acom**: integrated number of trigger events leading to coincidences in the anticommutator port. </p> <p>"datastore_H.h5" holds data acquired using horizontally polarized input light. <br> "datastore_P.h5" holds data acquired using horizontally polarized input light.</p> <p>The DataFrame "tomos.h5" contains the following columns:</p> <p>- **U**: The unitary intended to be implemented by the gadgets<br> - **U_fwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **U** gadget in forwards direction.<br> - **U_fwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **U** gadget in forwards direction.<br> - **Ur_fwd**: Unitary implemented by the **U** gadget in forwards direction, reconstructed using **U_fwd_tomoH** and **U_fwd_tomoP**<br> - **U_bwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **U** gadget in backwards direction.<br> - **U_bwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **U** gadget in backwards direction.<br> - **Ur_bwd**: Unitary implemented by the **U** gadget in backwards direction, reconstructed using **U_fwd_tomoH** and **U_fwd_tomoP**<br> - **V_fwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **V** gadget in forwards direction<br> - **V_fwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **V** gadget in forwards direction.<br> - **Vr_fwd**: Unitary implemented by the **V** gadget in forwards direction, reconstructed using **V_fwd_tomoH** and **V_fwd_tomoP**<br> - **V_bwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **V** gadget in backwards direction.<br> - **V_bwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **V** gadget in backwards direction.<br> - **Vr_bwd**: Unitary implemented by the **V** gadget in backwards direction, reconstructed using **V_fwd_tomoH** and **V_fwd_tomoP**.<br> - **fid_Ufwd_h**: Fidelity between the theoretically expected and experimentally obtained state U|H> in forwards direction.<br> - **fid_Ubwd_h**: Fidelity between the theoretically expected and experimentally obtained state U|H> in backwards direction.<br> - **fid_Vfwd_h**: Fidelity between the theoretically expected and experimentally obtained state V|H> in forwards direction.<br> - **fid_Vbwd_h**: Fidelity between the theoretically expected and experimentally obtained state V|H> in backwards direction.<br> - **fid_Ufwd_p**: Fidelity between the theoretically expected and experimentally obtained state U|P> in forwards direction.<br> - **fid_Ubwd_p**: Fidelity between the theoretically expected and experimentally obtained state U|P> in backwards direction.<br> - **fid_Vfwd_p**: Fidelity between the theoretically expected and experimentally obtained state V|P> in forwards direction.<br> - **fid_Vbwd_p**: Fidelity between the theoretically expected and experimentally obtained state V|P> in backwards direction.<br> - **fid_Ufwd_Ubwd_h**: Fidelity between the experimentally obtained states U|H> in forwards and backwards direction.<br> - **fid_Vfwd_Vbwd_h**: Fidelity between the experimentally obtained states V|H> in forwards and backwards direction.<br> - **fid_Ufwd_Ubwd_p**: Fidelity between the experimentally obtained states U|P> in forwards and backwards direction.<br> - **fid_Vfwd_Vbwd_p**: Fidelity between the experimentally obtained states V|P> in forwards and backwards direction.</p> <p>The format for polarization tomography data is:<br> [h,v,p,m,r,l] <br> where<br> **h** is the power acquired projecting the state in |H>,<br> **v** on |V>,<br> **p** on |+>,<br> **m** on |->,<br> **r** on |R>, and<br> **l** on |L>.</p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
ScienceDex guides
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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.