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4,230 results for “Energie”
SHEERM: Sustainable Household Energy and Environment Resources Management dataset
<p>This dataset represents a novel and extensive dataset featuring comprehensive cross-sectional data of household electrical load, energy cost, and on-premises solar energy production, directly linked to solar radiation and weather parameters. <br>The SHEERM dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SGD) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation.<br>Together with the SHEERM dataset, there is a paper that details the data collection process, including the sources and methodologies employed. Adhering to established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying various machine-learning approaches to the provided data.<br>The SHEERM dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition towards renewable energy and carbon neutrality.</p>
PtX - Die Zukunft der Energie im Wasserstoffatlas
<p>Funded by Federal Ministry of Education and Research in the project "Wasserstoffatlas Deutschland"</p> <p>The scematic was designed in order to understand the processes behind the hydrogen economy model in the hydrogen atlas.</p> <p><a title="https://wasserstoffatlas.de/en/potential-energy?pot=energy&nuts=nuts3&techProduct=elyAEL&calcBasis=EnVerf" href="https://wasserstoffatlas.de/en/potential-energy?pot=energy&nuts=nuts3&techProduct=elyAEL&calcBasis=EnVerf" target="_blank" rel="noopener">Wasserstoffatlas - Hydrogen Potential GER</a></p> <p>"The aim of the hydrogen atlas is to make research data on the potential, the current state of hydrogen and Power-to-X in Germany and the market ramp-up of the hydrogen economy publicly accessible.</p> <p><strong>With our interactive website</strong>, it is now possible to find locations of existing and planned plants and actors along the hydrogen value chain, identify potential and analyze opportunities. The comprehensive and clearly presented data sets are available free of charge to decision-makers from politics and industry, but also to interested members of the public."</p>
Artifact Description/Artifact Evaluation/Computational Artifact for paper, entitled Analytic Roofline Modeling and Energy Analysis of the LULESH Proxy Application on Multi-Core Clusters
We provide reproducibility initiative dependencies (Artifact Description or Artifact Evaluation or Computational Results Analysis) appendix. To allow a third party to duplicate the findings, this article provides our extensive performance data artifact and describes further details regarding the software environments, experimental design, and methodology employed for the results shown in the paper. The computational artifacts will enable experienced performance engineers to reproduce and interpret the data shown in the paper in the appropriate way and to follow the conclusions we draw from it.
Dataset for "Sustainable Disposal and End-of-Life Treatment of Battery Energy Storage Systems: An Environmental and Economic Case Study"
This study investigates the environmental and economic impacts of end-of-life (EOL) treatment for a 2.8 MWh/2.5 MW battery energy storage system (BESS) based on lithium-ion batteries (LIBs). It focuses on recycling pre-treatment processes for battery systems and recycling procedures for components like cooling systems, fire extinguishing systems, inverters, and the reuse of BESS containers and substations. A life cycle assessment (LCA) was employed to evaluate key environmental impacts, including climate change, eutrophication, and resource use. The study reveals substantial environmental benefits, particularly from recovering secondary materials like aluminium and copper, with recycling pre-treatment contributing significantly to overall benefits. Additionally, the economic analysis projects profits, emphasizing the advantages of locally sourcing critical raw materials. The research highlights the need for more sustainable recycling practices and provides insights for improving environmental and economic strategies in BESS management, offering guidance for future research and policy development in battery waste processing.
DRALOD D1.3 Results of performance testing of the prototype of energy recovery system data set
<p>Data set for delivery D1.3 Results of performance testing of the prototype of energy recovery system </p>
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids (Dataset)
<p><strong>Abstract</strong></p> <p>The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility. </p> <p><strong>Data Navigation</strong></p> <p>Please download, unzip and put somewhere for later benchmark results reproduction and data loading and performance evaluation for proposed methods.</p> <pre><code>wget https://zenodo.org/record/5130612/files/PSML.zip?download=1 7z x 'PSML.zip?download=1' -o./ </code></pre> <p><strong>Minute-level Load and Renewable</strong></p> <ul> <li>File Name <ul> <li>ISO_zone_#.csv: `CAISO_zone_1.csv` contains minute-level load, renewable and weather data from 2018 to 2020 in the zone 1 of CAISO.</li> </ul> </li> <li>- Field Description <ul> <li>Field `<em>time</em>`: Time of minute resolution.</li> <li>Field `<em>load_power</em>`: Normalized load power.</li> <li>Field `<em>wind_power</em>`: Normalized wind turbine power.</li> <li>Field `<em>solar_power</em>`: Normalized solar PV power.</li> <li>Field `<em>DHI</em>`: Direct normal irradiance.</li> <li>Field `<em>DNI</em>`: Diffuse horizontal irradiance.</li> <li>Field `<em>GHI</em>`: Global horizontal irradiance.</li> <li>Field <em>`Dew Point</em>`: Dew point in degree Celsius.</li> <li>Field `<em>Solar Zeinth Angle</em>`: The angle between the sun's rays and the vertical direction in degree.</li> <li>Field `<em>Wind Speed</em>`: Wind speed (m/s).</li> <li>Field `<em>Relative Humidity</em>`: Relative humidity (%).</li> <li>Field `<em>Temperature</em>`: Temperature in degree Celsius.</li> </ul> </li> </ul> <p><strong>Minute-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>case #: The `case 0` folder contains all data of scenario setting #0. <ul> <li>pf_input_#.txt: Selected load, renewable and solar generation for the simulation.</li> <li>pf_result_#.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>Field <em>`time`</em>: Time of minute resolution.</li> <li>Field <em>`Vm_###`</em>: Voltage magnitude (p.u.) at the bus ### in the simulated model.</li> <li>Field <em>`Va_###`</em>: Voltage angle (rad) at the bus ### in the simulated model.</li> <li>Field <em>`P_#_#_#`</em>: `P_3_4_1` means the active power transferring in the #1 branch from the bus 3 to 4.</li> <li>Field <em>`Q_#_#_#`</em>: `Q_5_20_1` means the reactive power transferring in the #1 branch from the bus 5 to 20.</li> </ul> </li> </ul> <p><strong>Millisecond-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>Forced Oscillation: The folder contains all forced oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li> info.csv: This file contains the start time, end time, location and type of the disturbance</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Natural Oscillation: The folder contains all natural oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li>info.csv: This file contains the start time, end time, location and type of the disturbance.</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>trans.csv <ul> <li> - Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li> - Field <em>`VOLT ###`</em>: Voltage magnitude (p.u.) at the bus ### in the transmission model.</li> <li> - Field <em>`POWR ### TO ### CKT #`</em>: `POWR 151 TO 152 CKT '1 '` means the active power transferring in the #1 branch from the bus 151 to 152.</li> <li> - Field <em>`VARS ### TO ### CKT #`</em>: `VARS 151 TO 152 CKT '1 '` means the reactive power transferring in the #1 branch from the bus 151 to 152.</li> </ul> </li> <li>dist.csv <ul> <li>Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li>Field <em>`####.###.#`</em>: `3005.633.1` means per-unit voltage magnitude of the phase A at the bus 633 of the distribution grid, the one connecting to the bus 3005 in the transmission system.</li> </ul> </li> </ul> </li> </ul>
Lietuvos namų ūkių apklausa energetikos klausimais (Lithuanian Household Survey on Energy Issues)
<p>Duomenų rinkinyje pateikiami reprezentatyvios Lietuvos namų ūkių apklausos (N=1008) apie Lietuvos namų ūkių energetikos situaciją ir su valstybės parama šioje srityje susijusias žinias. Apklausos klausimyną parengė Lietuvos energetikos instituto mokslininkai, o apklausos lauko darbus atliko UAB "Vilmorus" 2020 m. lapkričio mėn. 16 d. – gruodžio mėn. 7 d.</p> <p> </p>
Lithuanian Household Energy Expenditure and Energy Poverty Data, 2019
<p>This dataset provides data about household energy expenditure and energy poverty in Lithuania. The dataset contains detailed data about 5031 households and is based on the Lithuanian Survey on Income and Living Conditions (2019) micro dataset provided by Statistics Lithuania. It includes additional data derived from original survey data and energy poverty calculation results at household level.</p> <p>Duomenų rinkinyje pateikiami duomenys apie namų ūkių energijos išlaidas ir energijos nepriteklių Lietuvoje 2019 metais. Duomenų rinkinys apima 5131 namų ūkį. Rinkinio pagrindas - Pajamų ir gyvenimo sąlygų statistinio tyrimo duomenys, skelbiami Lietuvos Statistikos departamento. Duomenų rinkinys apima ir papildomus duomenis gautus remiantis originalios apklausos duomenimis bei energijos nepritekliaus skaičiavimų rezultatus namų ūkio lygmenyje.</p>
A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster
<p>Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way.</p> <p>Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households)</p> <p>RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing.</p> <p>Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv</p> <p>uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy</p>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
On energy transfer of parametric resonance for wave energy conversion
<p>Parametric resonance has been observed, both numerically and experimentally, in various studies of wave energy converters (WECs). A large motion in heave induces a periodic variation in the metacentric height of a WEC body, and, consequently, causes a harmonic variation in pitch/roll restoring coefficients, which can parametrically excite the pitch/roll modes. Current studies have a specific focus to determine the occurrence conditions of parametric resonance, by detecting the boundaries between stable and unstable regions in the parameter space. In the literature, some studies aim to make use of parametric resonance for improving power capture. In contrast, some studies try to suppress the effect of parametric resonance, as it reduces power capture efficiency. However, how energy transfers from one mode to another is not fully understood. This study aims to analyse the energy transfer between heave and pitch/roll modes when parametric resonance occurs. A generic cylindrical point absorber is studied as a WEC floater to considering non-linear wave-structure interaction, including non-linear Froude-Krylov and viscous forces. A heave-pitch-roll three-degree-of-freedom model is derived for numerical study of the energy transfer between different operation modes.</p>
Carbon Budget Scenarios for Ireland's Energy System, 2021-50
<p>Carbon Budget Scenarios for Ireland's Energy System, 2021-50, calculated with the TIMES-Ireland model.</p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Climate change impacts on energy demand
<p>Climate change impacts on energy demand by energy carrier (electricity, natural gas, and petroleum) and sector (agriculture, industry, residential, and commercial).</p>
Scores for calculating automated FAIR assessments in the low carbon energy domain
<p>Results for an automated FAIR assessment of 80 databases from the low carbon energy domain. The assessment was performed with the help of the FAIR maturity evaluation service of Wilkinson et al. The FAIR status with respect to 16 FAIR criteria is listed. The scores are defined to be consistent with the FAIR assessment tool of the Australian Research Data Commons. More details can be found in an additional publication on Zenodo as well as in an upcoming publication by Schwanitz et al.</p>
Dataset: Import options for chemical energy carriers from renewable sources to Germany
<p>This dataset contains results and additional data related to the publication "Import options for chemical energy carriers from renewable sources to Germany".</p> <p>Files containing major results / important cost input data:</p> <ul> <li><strong>results.csv</strong>: Contains major model results for all scenarios as CSV file (seperator is ';', all fields are quotes using double quotation marks '"'). Can be explored using standard software like Excel/Libre Office or other tools.</li> <li><strong>costs.zip</strong>: Technology specific input cost assumption for 2030, 2040 and 2050.</li> </ul> <p>The dataset further contains the following archives related to the model structure as contained in the software repository (GitHub):</p> <ul> <li><strong>config.zip</strong>: File contents of the <em>config/</em> folder of the model directory. Configuration files for running the model used by the publication.</li> <li><strong>data.zip</strong>: File contents of the <em>data/</em> folder of the model directory. Includes distance specifications, conversion efficiencies, details on shipping transport. Also contains (with this version) the cost data (same as in <em>costs.zip</em>).</li> <li><strong>resources.zip</strong>: Some file contents of the <em>resources/</em> folder of the model directory. Most files in this folder are automatically recreated if the <em>Snakemake</em> workflow is executed. The files in this archive are the files created by GlobalEnergyGIS (RES supply time-series and demand data for investigated regions) which is difficult to setup and are thus provided here as an optional dataset for download.</li> <li><strong>results.zip</strong>: Optimised energy system models (<a href="https://pypsa.readthedocs.io/en/latest/">PyPSA</a> networks, for PyPSA version v0.19.3) for all scenarios (default 10% WACC, optimistic 5% WACC, scenarios for sensitivity analysis), energy supply chains (ESCs) and exporting countries. For each network an additional results.csv exists containing a number of key results extracted from each network. Also contains the combined <em>results.csv</em> file as <em>results/results.csv</em> for all scenario runs.</li> </ul>
Dataset of 20 energy prosumers with flexibility data, distributed generation and energy storage
<p>The dataset has 20 prosumers, each with three appliances to provide flexibility for DR events, two PV generation resources, and an energy storage system. The values represent a day using 15 minutes reading periods. All the values are expressed in W, and the matrixes were created as [ time_period x info].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Result data related to "Cost-potential curves of onshore wind energy: the role of disamenity costs"
<p>This dataset estimates the impact of incorporating disamenity costs of wind onshore in Europe (in addition to technology cost). The data haset has been generated and used for the publication:</p> <blockquote> <p>Ruhnau, O., Eicke, A., Sgarlato, R., Tröndle, T., Hirth, L., 2022. Cost-potential curves of onshore wind energy: the role of disamenity costs. Environmental and Resource Economics. DOI: <a href="https://doi.org/10.1007/s10640-022-00746-2">10.1007/s10640-022-00746-2</a></p> </blockquote> <p>The corresponding code is published on <a href="https://github.com/timtroendle/wind-onshore-cost-potential">GitHub</a>.</p> <p>The dataset includes:</p> <ol> <li>Maps that exhibit the population count within a predefined distance (e.g., "population-within-1km.tif") and the resulting disamenity costs ("disamenity-cost.tif")</li> <li>Tables that summarize the engineering and disamenity costs faced at each potential turbine location in the EU (e.g., "turbines-AT.csv")</li> </ol>
Dataset supporting publication: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."
<p>DATASET suporting: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."</p> <p>Piccinini, Alessandro; Hajdukiewicz, Magdalena; D'Angelo, Letizia; Blanes, Luis Miguel; Keane, Marcus M.</p> <p>This paper presents a novel Reduced Order grey box Model (ROM) methodology, based on a ResistorCapacitor (RC) network, which supports the creation of the baseline energy consumption and the estimation of energy savings due to Energy Conservation Measures (ECMs) under the Measurement and Verification protocol. Within this scope, a description of the RC network, including a calculation of the parameters’ needed to execute the ROM, are presented. This ROM methodology is demonstrated on an educational building located in Sant Cugat, Spain as part of the H2020 GEOFIT project. The results presented in this paper demonstrate that the ROM is sufficiently accurate for the creation of the baseline energy consumption and for estimating the energy savings of different ECMs.</p>
Micheluz et al energy dispersive X-ray-spectroscopy data
<p><strong># Micheluz et al energy dispersive X-ray-spectroscopy data</strong></p> <p><strong>## paper: https://doi.org/10.3390/pathogens11121462</strong></p> <p><strong>### Dataset: </strong></p> <p>Dataset1_Micheluz-et-al.csv</p> <p>This is a CSV file with 206 lines and 12 columns.</p> <p>Data are energy-dispersive X-ray spectroscopy data as weight%</p> <p>Explanation of the heading: Site, the Italian city where the samples of Eurotium halophilicum were collected;</p> <p>Structure, the features analysed (EPS, conidia, background); presence of crystal (TRUE/FALSE) indicates if in the areas/samples were detected biogenic crystals;</p> <p>C, carbon; O, oxygen; Na, sodium; P, phosphorus; S, sulphur; Cl, chlorine, K, potassium; Ca, calcium; Au, gold; Total, the sum of all the elements.</p> <p> </p> <p>Dataset1_Micheluz-et-al.csv</p> <p>This is a CSV file with 24 lines and 8 columns.</p> <p>Data are energy-dispersive X-ray spectroscopy data as atomic%</p> <p>Explanation of the heading: ID, is the biogenic crystal analysed; </p> <p>C, carbon; O, oxygen; Na, sodium; S, sulphur; Cl, chlorine, Ca, calcium; Total, the sum of all the elements.</p> <p><strong>### Sampling</strong></p> <p>The mycelium samples analysed with Energy-dispersive X-ray spectroscopy were obtained by pressing a carbon-based, impurity-free adhesive tape (FungiTapeTM, Scientific Device Lab., Inc Glenview, IL, USA) onto the fungal colonies found on the spines of books in five Italian libraries in Turin, Venice, Genova and Rome (2 libraries in Rome).</p> <p><strong>### Methods</strong></p> <p>Energy-dispersive X-ray spectroscopy was performed with an INCA Oxford 250 system, maintaining the electron beam at 20 keV, with a mean working distance to the sample of 12.5 mm. The electron beam could be focused on very small areas, in the order of nm2, allowing a surface resolution that readily resolves objects that are a few tens of nm in dimension. This way, a database was obtained with repeated observations of the composition of conidia and other fungus structures carried out on samples from the covers of different books. Some samples were analysed with energy-dispersive X-ray spectroscopy also after metallisation in order to be able to focus on peculiar structures like the (apparently biogenic) crystals. When this was the case, the spectra obtained contained gold, also present in the background.</p> <p>The calibration of the apparatus was based on the standards CaCO3, SiO2, albite, MgO, Al2O3, GaP, FeS2, wollastonite, feldspar MAD-10, Ti and Fe, supplied by Agar Scientific Ltd. (Stansted, UK) and the conventional ZAF correction (atomic number Z, absorption A, fluorescence F) from the Oxford INCA 250 software was applied to the measurements to convert apparent concentrations (raw peak intensity) into (semi-quantitative) concentrations corrected for inter-element matrix effects.</p> <p><strong>### Labels Used</strong></p> <p>Conidia = part of the fungal mycelium analysed</p> <p>EPS = Extracellular polymeric material present in the mycelium</p> <p>Background = the adhesive tape (made of carbon) used to collect the mycelium and prepare the samples for observation with scanning electron microscopy</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.