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87 results for “Energy Storage”
Core-Hole Spectroscopy of Energy Conversion and Storage-Related Phosphorus Compounds Using Soft and Hard X rays (Experimental XANES dataset)
<p>Dear readers,</p> <p>In the following, we provide the complete experimental X-ray absorption near-edge structure (XANES) spectroscopy dataset given in our investigation of: "Core-Hole Spectroscopy of Energy Conversion and Storage Related-Phosphorus Compounds Using Soft and Hard X-rays". This dataset includes partial fluorescence yield (PFY)-XANES at P <em>K</em>-edge and P <em>L</em><sub>2,3</sub>-edge of 9 solid P-containing compounds with different chemical environments and oxidation states ranging from P(-III) to P(V), namely: GaP, InP, red phosphorus (Red-P), H<sub>3</sub>PO<sub>3</sub>, Na<sub>2</sub>H<sub>2</sub>P<sub>2</sub>O<sub>6</sub>, H<sub>3</sub>PO<sub>4</sub>, KH<sub>2</sub>PO<sub>4</sub>, Na<sub>2</sub>HPO<sub>4</sub>, InPO<sub>4</sub>. Additionally, P <em>K</em>-edge XANES spectra of aqueous P-containing acids: 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>4</sub>, 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>3</sub>, as well as the solution mixture of 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>3</sub> + 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>4</sub>, and 0.1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>3</sub> + 1 mol dm<sup>-3</sup> H<sub>3</sub>PO<sub>4</sub>.</p>
An investigation on the effect of high energy storage anchor on surrounding rock conditions
High pre-tension bolt is an effective strata control technique and is the key to ensure the stability of anchorage and roadway. Based on the performances of high energy storage tension rock bolts in different rock properties, this study proposed a constitutive model to describe the energy balance of anchor under uniaxial compression. UDEC was used to simulate the behaviour of anchor in coal under uniaxial compression and the results were analysed to study the rock mechanical properties, degree of damage and energy evolution. Simulation results showed that tension rock bolts can improve the mechanical properties and energy storage capacities of anchor. The energy evolution was divided into three stages: i) the external work was stored in the form of elastic strain energy (Ue) in the anchor prior to the yielding strength; ii) the elastic strain energy reached its maximum near the peak strength; iii) Energy was dissipated from fracture friction (Wf ), plastic deformation (Wp) and acoustic emission (Ur) during post-peak stage. The installation of tension rock bolts was more suitable for medium hard rock (e.g. sandy mudstone), whereas it was not effective for hard rock (e.g. sandstone).
Data set related to the manuscript "Simulations of ionic liquids confined in surface-functionalized nanoporous carbons: Implications for energy storage"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Simulations of ionic liquids confined in surface-functionalized nanoporous carbons: Implications for energy storage". Examples of input files for the three systems simulated.</p>
Data and results related to "Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements and Effects on Capacity Adequacy - Energy"
<p>The file includes data used for the analysis and results coming from the study (which was focused on the Italian "Nord" bidding zone). In particular:</p> <p>(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.</p> <p>(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).</p> <p>(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements</p>
2D Germanane-MXene Heterostructures for Cations Intercalation in Energy Storage Applications
<p>Raw Data of the full Article "2D Germanane-MXene Heterostructures for CationsIntercalation in Energy Storage Applications"</p> <p> </p>
Engineering the Compositional Architecture of Core-Shell Upconverting Lanthanide-Doped Nanoparticles for Optimal Luminescent Donor in Resonance Energy Transfer: The Effects of Energy Migration and Storage
<p>Förster Resonance Energy Transfer (FRET) between single molecule donor (D) and acceptor (A) is well understood from fundamental perspective and is widely applied in biology, biotechnology, medical diagnostics and bio-imaging. However, the reliability of molecular FRET measurements can be affected by numerous artefacts which eventually hamper quantitative and reliable analysis, mostly due to issues with the donor and acceptor molecules. Lanthanide doped upconverting nanoparticles (UCNPs) have demonstrated their suitability as alternative donor species. Nevertheless, while they solved most disadvantageous features of organic donor molecules, such as photo-bleaching, spectral cross-excitation and emission bleed-through, the fundamental understanding and practical realizations of bio-assays with UCNP donors remain challenging. Among others, the actual donor ions in individual donor UCNPs are the numerous activator ions randomly distributed in the nanoparticle at various distances to acceptors anchored on the nanoparticle surface. Further, the power dependent, complex energy transfer upconversion and energy migration between sensitizing and activating lanthanide ions within UCNPs complicate the decay based analysis of <strong><em>D</em></strong>-<strong><em>A</em></strong> interaction. In this work, the assessment of designed virtual core-shell nanoparticle (VNP) models led us to the new designs of UCNPs, such as …@Er, Yb@Er, Yb@YbEr, which were experimentally evaluated as donor nanoparticles and compared to the simulations. Moreover, the specific properties of lanthanide-based upconversion motivated us to analyze not only steady-state luminescence and luminescence decay responses of both the UNCP donor and the sensitized acceptor, but also the effects of their luminescence rise kinetics upon RET was discussed in newly proposed disparity measurements. The presented studies help to understand the role of energy-transfer and energy migration between lanthanide ion dopants (due to their concentration and spatial distribution) and how the architecture of core-shell UCNPs affects their performance as FRET donors to organic acceptor dyes.</p>
CEC EPC-19-056 Long-Duration Energy Storage Modeling Dataset
<p>Modeling dataset to study the value of long-duration energy storage (LDES) as part of the California bulk electricity system. Based on California Public Utilities Commission (CPUC) Integrated Resource Planning (IRP) proceeding 2019-2020 cycle data.</p>
Surface science and liquid phase investigations of oxanorbornadiene/oxaquadricyclane ester derivatives as molecular solar thermal energy storage systems on Pt(111) [doi: 10.1063/5.0158124]
<p>Primary data, meta data, and corresponding lists of figures & tables are included. [doi: 10.1063/5.0158124]</p>
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </p>
The role of energy storage technologies in the context of the Swiss energy transition (SwissStore)
<p>The role of energy storage is subject to intense debates at the national and international level, reflecting a lack of consensus about the techno-economic potential and respective merits of the various energy storage technologies. This is largely due to the wide spectrum of possible applications, evolving technology and unclear market conditions. In the context of the SwissStore project we studied energy storage in different contexts and from different perspectives, i.e.<br> • in households, in combination with photovoltaic panels (coupled with batteries and thermal storage), taking the perspective of the private investor<br> • in all buildings (all sectors, also with PV), with a focus on increased short-term flexibility, thereby also considering other strategies next to storage (e.g. demand-side response) and taking the perspective of the social planner.<br> In both cases, optimisation frameworks were developed for Switzerland and different scenarios were studied for the next decades.</p>
Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment
<p>Data used in "Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment".</p>
Original data for publications: Direct solution–based synthesis of the Na4(B12H12)(B10H10) solid electrolyte, Experimental investigation of Mg(B3H8)2 dimensionality, materials for energy storage applications, Thermal Conversion of Unsolvated Mg(B3H8)2 to BH4– in the Presence of MgH2
<p>Original data for the following papers (Only part of University of Geneva, synthesis part):</p> <p>Direct solution–based synthesis of the Na4(B12H12)(B10H10) solid electrolyte<br> Gigante, A.; Duchêne, L.; Moury, R.; Pupier, M.; Remhof, A.; Hagemann, H.<br> ChemSusChem 2019, 12, 4832-4837</p> <p>Experimental investigation of Mg(B3H8)2 dimensionality, materials for energy storage applications<br> Romain Moury, Angelina Gigante, Arndt Remhof, Elsa Roedern and Hans Hagemann<br> Dalton Trans., 2020,49, 12168-12173, https://doi.org/10.1039/D0DT02170A</p> <p>Thermal Conversion of Unsolvated Mg(B3H8)2 to BH4– in the Presence of MgH2<br> Angelina Gigante, Noemi Leick, Andrew S. Lipton, Ba Tran, Nicholas A. Strange, Mark Bowden, Madison B. Martinez, Romain Moury, Thomas Gennett, Hans Hagemann, and Tom S. Autrey<br> ACS Appl. Energy Mater. 2021, 4,3737-3747. https://doi.org/10.1021/acsaem.1c00159</p> <p> </p>
Simulation Results of the Distributed Schedule Optimization with Energy Storages using EO-COHDA
<p>This dataset contains the result of the evaluation of an approach to integrate energy storages in distributed flexibility negotiations. The data was created using the implemented approach on https://gitlab.com/digitalized-energy-systems/models/eo-cohda. To work with this results we highly recommend to use eo-cohda as well, because it provides a lot convenient utility functions for this.</p> <p>The dataset has been divided in two parts:</p> <ol> <li>the result of the negotiation, <ul> <li>format: hdf, readable using hdf-viewers/python</li> </ul> </li> <li>the generated schedules of the energy storages used for the negotiation. <ul> <li>format: binary, pickled real power schedules, readable using eo-cohda's utility methods.</li> </ul> </li> </ol> <p> </p>
Thermal energy storage - heating the north, cooling the south
<p><b>Abstract</b></p><p class="dhik-abstract-content">Within the energy turnaround worldwide energy storage is one of the key components for increasing the potential of new renewable energy sources. Heating and cooling is responsible for 50% of the energy demand with a strong increase in cooling. Therefore, the Thermal Energy Storage research group at HSLU (www.hslu.ch\tes) optimizes and develops thermal storage systems.</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li><b>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (<a href="#collapseTwo">Video</a>)</b></li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Data: Green ammonia imports could supplement long-duration energy storage in the UK
<div> <p>Data used for the analysis presented in 'Green ammonia imports could supplement long-duration energy storage in the UK'. </p> <p> </p> </div>
Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application
<p>Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application". The dataset contains relevant data and figures from the publication. Description on how to work with the dataset is given in the readme file. DOI for the original paper: DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3RA07335A">https://doi.org/10.1039/D3RA07335A</a>.</p>
Challenges to Profitability for Energy Storage in High Renewable Energy Systems
<p>This figure illustrates the impact of renewable energy source (RES) penetration on electricity price spreads and the profitability of energy storage systems.</p> <p>In scenarios with a low share of RES, the price spread is significant. During low-demand periods, electricity is predominantly supplied by cost-effective RES, leading to lower prices. Conversely, during high-demand hours, traditional power plants with higher operational costs are required, resulting in elevated prices. Energy storage systems can capitalize on this large price spread by charging during low-price periods and discharging during high-price periods, thereby maximizing their profits.</p> <p>In contrast, with a high share of RES, both low and high-demand periods are largely covered by renewable sources. This extensive reliance on RES minimizes the price differential between these periods, resulting in a much smaller price spread. Consequently, the potential for storage systems to profit from price arbitrage is reduced, as the opportunities to buy low and sell high diminish.</p>
Data for the Article "Maturity Assessment of Grid-Scale Flexibility and Energy Storage Services Towards a Decarbonized Europe"
<p>This dataset includes the data collected form various stakeholders regarding grid-scale flexibility and energy services as part of the SINNOGENES Project.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.</p>
Raw Data for Energy Consumption Comparison of DMA-Based and FatFs Storage Systems on Wearable Devices
<p>This dataset contains raw oscilloscope measurements comparing the energy consumption of a Direct Memory Access (DMA)-based storage system versus the FatFs file system for wearable devices. The data was collected as part of the study "Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner".</p> <p>The dataset includes voltage drop measurements across a 2-ohm shunt resistor, captured using an Analog Discovery 2 digital oscilloscope at a 500 kHz sampling rate. Measurements were taken under various conditions:</p> <ul> <li>Storage systems: DMA-based (proposed) and FatFs</li> <li>SD card capacities: 4 GB and 8 GB</li> <li>Write frequencies: 2 Hz and 5 Hz (referring to the frequency of writing a specific data block of 15,872 bytes)</li> <li>With and without a smoothing capacitor</li> </ul> <p>Each CSV file contains 20 million samples, equivalent to 40 seconds of data acquisition. File names encode the experimental conditions, including the storage system, write frequency, number of samples, acquisition rate, acquisition time, data format, SD card size, and absence of the smoothing capacitor.</p> <p>The data is organized into two main folders:</p> <ol> <li>"cap": Contains measurements with the smoothing capacitor</li> <li>"no_cap": Contains measurements without the smoothing capacitor</li> </ol> <p>This raw data can be used to reproduce the energy consumption and write speed analyses presented in the article, as well as for further investigation into the performance of embedded storage systems for wearable devices.</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.