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800 results for “mixtures”
Scanned images of monocultures and mixtures of six grassland plant species roots, and of simulated fine roots
<p>Soil core samples were taken from a multi-species grassland experiment with field plots of monocultures and mixtures of six grassland plant species: <em>Lolium perenne</em> L. (PRG),<em> Phleum pratense</em> L. (TIM), <em>Trifolium pratense</em> L. (RC), <em>Trifolium repens</em> L. (WC), <em>Cichorium intybus </em>L. (CHIC), and <em>Plantago lanceolata </em>L.. The multi-species plots had a two species mixture with <em>Trifolium repens </em>L. and<em> Lolium perenne</em> L. (PRGWC), and a 6 species mixture with all species mentioned above. The cores were separated into soil depths of 0-10 cm, 10-15 cm and 15-20 cm and the roots separated from the soil.</p> <p>A ground-truth image set was created to simulate fine roots using fishing line. The fishing line used was a clear copolymer monofilament (Greys<sup>TM</sup> Greylon Tippet Material 3 lb), measured using a scanning electron microscope (Hitachi SU8200) to be 0.14 mm in diameter. The fishing line was used in its clear colour or coloured black using a permanent marker to simulate unstained and stained fine roots respectively. The fishing line was cut into lengths of 30 cm or 5 cm. </p> <p>Roots and fishing line were scanned using an Epson Perfection V800 flatbed scanner at 600 dpi. </p> <p>The Roots ZIP file contains a folder for the scanned root images and the Line zip file contains a folder with the scanned fishing line. The excel spreadsheet describes the naming convention for the images.</p> <p>Further details about the root sampling and image acquisition can be found in the publication that analyses these images: <a href="https://doi.org/10.1002/ppj2.20034">https://doi.org/10.1002/ppj2.20034</a></p>
[DCASE2022 Task 3] Synthetic SELD mixtures for baseline training
<p><strong>DESCRIPTION:</strong><br> <br> This audio dataset serves serves as supplementary material for the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">DCASE2022 Challenge Task 3: Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes</a>. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the <a href="https://zenodo.org/record/5476980">TAU-NIGENS Spatial Sound Scenes 2021</a> dataset for the <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection-results">DCASE2021 Challenge Task 3</a>. </p> <p>The SELD task setup in DCASE2022 is based on spatial recordings of real scenes, captured in the <a href="https://zenodo.org/record/6387880">STARS22</a> dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the <a href="https://github.com/sharathadavanne/seld-dcase2022">baseline model</a>, and they are shared for reasons of reproducibility. For more details on the task setup, please refer to the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">task description</a>.</p> <p>Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see:</p> <ul> <li>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</li> </ul> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li><strong>13 target sound classes</strong> (see task description for details)</li> <li>The sound event samples are sources from the <strong><a href="https://zenodo.org/record/4060432">FSD50K</a></strong> dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here.</li> <li><strong>1200</strong> 1-minute long spatial recordings</li> <li>Sampling rate of<strong> 24kHz</strong></li> <li>Two 4-channel recording formats, first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array (<strong>MIC</strong>)</li> <li>Spatial events spatialized in <strong>9 unique rooms</strong>, using measured RIRs for the two formats</li> <li>Maximum <strong>polyphony of 2</strong> (with possible same-class events overlapping)</li> <li>Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a <strong>separation into a training and testing split</strong>, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only).</li> <li>The training split is indicated as <strong>fold1</strong> in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K.</li> <li>The testing split is indicated as <strong>fold2</strong> in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K.</li> <li>Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup.</li> </ul> <p><strong>FSD50K SELECTION:</strong></p> <p>The list of selected sound event recordings is included along the recordings and metadata, as <strong>FSD50K_selected.txt</strong>. Each line in the text file has the following structure:</p> <pre><code>[target_label]/[train/test]/[FSD50K_label]/filename.wav</code></pre> <p>with an example:</p> <pre><code>domesticSounds/train/Boiling/16584.wav</code></pre> <p>meaning that the file 16584.wav from FSD50K, with the <em>Boiling</em> label of FSD50K, is included in the samples for the training split of those synthetic recordings, and it is mapped to the target class of <em>domestic sounds. </em>Note that there can be multiple FSD50K labels mapped the same target class. Also note that if these are downloaded from FSD50K, and a folder structure is created that replicates the structure in the list, the resulting folder can be used out-of-the-box with the scene generator to generate new mixtures with the same or different parameters.</p> <p>Note that no sounds form FSD50K have been selected for the <em>Music</em> target class. Background and pop music tracks from the public domain have been cropped and used instead.</p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Spectral induced polarization of non-consolidated heterogeneous clay mixtures
<p>We present a spectral induced polarization dataset on heterogeneous mixtures of illite and red montmorillonite, with two longitudinal, and one transversal arrangement. Additionally, there is a 50-50% in volume content homogeneous mixture of illite and red montmorillonite.</p> <p>Each file has its header, describing each column. The ReadMe file also explains the content and format of each dataset.</p>
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Supplementary data and codes for "Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures"
<p>This file contains the data and codes used in the paper</p> <p>“Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022.</p> <p>It includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet.</p>
Experimental characterization of transversal-heterogeneous clay mixtures by the spectral induced polarization method
<p>In this folder you will find multiple datasets (*.txt) from SIP measurements of transversal-heterogeneous clay mixtures using spectral induced polarization acquired between April and May 2022. Additionally, we include two python codes to read and process the data.</p> <p>SIP_Plot_ReWrite.py is a python program aimed to process a .res file from a SIP Fuchs III.<br> It gives a .txt file with the frequency, the resistivity, the phase and the associated errors.<br> In order for the program to give the resistivity, you will need to enter the geometric factor of the studied sample.</p> <p><br> The six text files in the folder (excluding README.txt) were created using SIP_Plot_ReWrite.py.</p> <p>IL_1by1.txt and IL_1by1_V2.txt are from two different homogeneous mixtures of illite and water with a concentration of initially 0.01 mol/L of NaCl.<br> MtR_1by1.txt is from a homogeneous mixture of red montmorillonite and water with a concentration of initially 0.01 mol/L of NaCl.</p> <p>IL_MtR_1by2.txt, IL_MtR_1by4.txt and IL_MtR_1by8.txt are from three transversal-heterogeneous mixtures of illite and red montmorillonite with water containing a concentration of initially 0.01 mol/L of NaCl.</p> <p><br> For IL_MtR_1by2.txt, there was one portion of each clay types, occupying a half of the cylindrical container each.</p> <p><br> For IL_MtR_1by4.txt, there was two portions of each clay types, occupying a quarter of the cylindrical container each.</p> <p><br> These two samples were made using the same mixtures as for IL_1by1.txt and MtR_1by1.txt.</p> <p><br> For IL_MtR_1by8.txt, there was four portions of each clay types, occupying an eighth of the cylindrical container each.<br> This sample was made using the same mixtures as for IL_1by1_V2.txt and MtR_1by1.txt.</p> <p><br> TestDoubleColeColeFit.py is a python program which optimizes a double Cole-Cole model by multiplication on SIP data.<br> This program needs a file with the same structure as the .txt file made by SIP_Plot_ReWrite.py.</p>
Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Laboratory Tests
<p>A study was conducted to evaluate the repeatability of the Flexibility Index of asphalt mixtures obtained according to AASHTO TP-124-16. Three asphalt concrete samples were mixed and compacted using the Superpave Gyratory Compactor in one laboratory. The samples were then cut to specific dimensions for semi-circular bend testing based on the AASHTO Specifications at a single laboratory using a dedicated cutting equipment. The samples were randomized and distributed equally among three different testing labs.</p> <p>The process was repeated three times and in some instances the rate of loading was varied.</p> <p>This experiment allowed to study the repeatability of the the Flexibility Index</p>
Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Field Material
<p>The data contained herein is part of a study conducted with support from the Utah Department of Transportation. In the study, seven asphalt mixtures from across the state of Utah were collected at the plant (prior to delivery) and at laydown (prior to compaction). The mixtures were sealed in metal containers and brought to three different laboratories where the asphalt mixtures were compacted using a Superpave gyratory compactor into cylinders. Each cylinder was cut using a masonry saw to create semi-circular samples with a notch in the middle based on the specification from AASHTO T124-16. The samples were tested following the procedures outlined in the specification with some exceptions where the loading rate was changed. The results were used to developed specification limits.</p>
Metrics for two-sample tests: results on Mixture of Gaussians and Correlated Gaussians models
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/GenerativeModelsMetrics">GenerativeModelsMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Dataset related to publication: Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures
<p>Dataset related to the publication:</p> <p><em>Giuseppe Emanuele Lio,</em><em> Sara Levorin</em><em>, Atakan Erdoğan</em><em>, Jérémy Werlé</em><em>, Alain J. Corso</em><em>, Luca </em><em>Schenato</em><em>, Diederik S. Wiersma</em><em>, Marco Santagiustina</em><em>, Lorenzo Pattelli</em><em>, Maria Guglielmina Pelizzo. </em><em>Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures, accepted and in publishing on International Journal of Thermophysics. </em>DOI: 10.1007/s10765-024-03439-8</p> <p>The repository contains the temperature, relative humidity, irradiance, sky temperature and net cooling power measurements in the file named "DATA_IJT_Nanoporous_Film_ Layer.txt" and the python script (Data_analizer.py) to analyze them as done in the paper.</p> <p> </p> <p>The repository also contain the Matlab workspace (IJT_data.mat)and code (Data_R_ATR_analyzer)to evaluate all the sample reflectance and FTIR measurements. Moreover, the extra measurements performed on the Celgard thin film are available in the workspace "Celgard_UV_VIS_NIR_T.mat" and "R_Celgard.mat". Moreover in Version 2 all the used data for Reflectance and Transmittance are also available in "txt" format for a simple and fast usage. </p> <p> </p> <h2>Notes (English)</h2> <div> <table> <tbody> <tr> <td> <p>This work was partly supported by</p> <ul> <li>European Project 21GRD03 PaRaMetriC, which received funding from the European Partnership on Metrology, co-financed by the European Union's Horizon Europe Research and Innovation Programme, and from the Participating States.</li> <li>European Union - PON Research and Innovation 2014-2020 in accordance with Article 24, paragraph 3a), of Law No. 240 of December 30, 2010, as amended, and Ministerial Decree No. 1062 of August 10, 2021</li> </ul> </td> </tr> </tbody> </table> </div>
Data of uranyl-, Nd-, Ce-hydrolysis and their mixtures induced by thermal decomposition of urea
<p>The hydrolysis of UO<sub>2</sub><sup>2+</sup>, Nd<sup>III</sup> , Ce<sup>III</sup> and Ce<sup>IV</sup> cations, induced by thermal decomposition of urea, was studied. Moreover, we investigated binary mixtures of uranyl and the lanthanides, as well as ternary mixtures of uranyl and both lanthanides using Ce<sup>III</sup> or Ce<sup>IV</sup>. The impact of the urea content and the temperature on the reaction kinetics and the formed precipitate was evaluated and the results are in depth discussed in <a href="https://doi.org/10.1002/ejic.202100453">our article</a>, some results are also summarised in <a href="https://doi.org/10.5281/zenodo.5034714">this presentation</a>.</p> <p>Uranyl ions precipitated as ammonium diuranate (ADU) with different stoichiometry under the applied conditions. Nd<sup>III</sup> and Ce<sup>III</sup> cations showed a comparable pH evolution during hydrolysis and <em>Ln</em>CO<sub>3</sub>OH products were identified, whereas Ce<sup>IV</sup> hydrolysed at a lower pH and formed nanocrystalline CeO<sub>2</sub>. Depending on the urea content, a partial co-precipitation was observed for mixtures of UO<sub>2</sub><sup>2+</sup> and Nd<sup>III</sup>. The products of Ce<sup>III</sup> and Ce<sup>IV</sup> hydrolysis were also identified in the precipitates of binary uranyl and cerium mixtures. For ternary U/Nd/Ce mixtures, a simultaneous precipitation of Nd<sup>III</sup> and Ce<sup>III</sup> and a partial incorporation of the <em>Ln</em> phase into the ADU phase was observed, whereas the presence of Ce<sup>IV</sup>/CeO<sub>2</sub> resulted in three individual phases. The precipitation reaction was followed by monitoring the pH evolution and the metal concentration in the supernatant, applying UV/Vis and ICP-MS. The formed precipitates were characterised by XRD and SEM.</p> <p>The data accrued during the study are part of this data set. Please note that the provision of this dataset is based on a voluntary basis. You are welcome to use the data from this set in your work. If you do so and publish resulting findings, please cite our work as required by the license.</p>
Synthetic Escherichia coli mixture samples with variable coverage
<p>This dataset contains the synthetic mixture samples and reference sequences - as well as the appropriate metadata - that were originally used in the 2021 revision of the mSWEEP manuscript.<br> <br> There are 87 samples in total, each containing 100bp paired-end Illumina sequencing reads from 10 different <em>Escherichia coli </em>strains from 10 different lineages. The number of reads is set so that the sequencing coverage of the individual strains varies between 50x and 0.10x and sums up to 100x.</p>
Primary data: Signal enhancement of hyperpolarized 15N sites in solution — increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures
<p>Primary data for DOI: 10.1002/nbm.4787</p> <p>NMR in Biomedicine. 2022;e4787</p> <p>Title: Signal enhancement of hyperpolarized 15N sites in solution—increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures</p> <p>Authors: Ayelet Gamliel, David Shaul, J. Moshe Gomori, Rachel Katz-Brull</p> <p>Description:</p> <p>These primary datasets contain data presented in the above publication and consist of:</p> <p>1. 15N-NMR spectra in solutions</p> <p>2. 13C polarization buildup data in solid-state</p> <p>3. 13C microwave profiles in solid state</p> <p>Please consult the Archive Guide.</p>
Thermodynamic properties of ammonia-water (NH3H2O mixture). In Esperanto
<p>Thermodynamic data for the ammonia-water mixture are adapted from: Ibrahim, O. M. (1993). Thermodynamic properties of ammonia-water mixtures. In ASHRAE Transactions: Symposia (Vol. 93, p. 1495). <br> <br> </p>
Dataset for paper "Impact of formulation of photocurable precursors mixtures [...]"
<p>Ion exchange membrane designed for application in photo-electrochemical device for CO2 valorisation. Data on membrane development, characteristics and performance are destined for publication in a peer-reveiwed article. The raw data were generated using GC, electrochemical analysis (LSV, CV), and tensile testing machine</p>
Data and code from: A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate, a greenhouse experiment at Dickinson College in Carlisle, PA, USA, 2021.
Data and R code associated with a greenhouse study investigating the influence of water stress on growth, root traits, and biomass of rye and crimson clover seedlings grown separately or together. Data were collected in the Dr. Inge P. Stafford Greenhouse of Dickinson College (Carlisle PA, USA) in June 2021.
Individual datasets investigating combined toxicity of binary mixtures in bees from laboratory tests
<p>This excel file (DOI: https://doi.org/10.5281/zenodo.3383713) provides the individual datasets on binary mixture toxicity (mortality) in bees classified according to route and exposure patterns (i.e. oral, contact, acute and chronic) and mortality endpoints (e.g.LD<sub>50</sub>, LC<sub>50</sub>) for the honeybee (<em>Apis mellifera</em>) and wild bee species (<em>Osmia bicornis</em>, <em>Bombus terrestris</em>). 218 individual binary mixtures were collected and included in the statistical analyses with the majority of toxicological endpoints reported as lethal doses or concentrations (e.g. LD<sub>50</sub>, LC<sub>50</sub>,) for pesticides or pesticides and veterinary drugs combinations with 133, 44 and 41 mixtures reporting acute contact toxicity (i.e. topical application), chronic oral toxicity and acute oral toxicity, respectively. Combined toxicity data for binary mixtures were available as dose response data in honeybees for acute contact toxicity (n=92) and acute oral toxicity.</p> <p>The full data collection and analysis of binary mixtures are described in Carnesecchi et al., 2019 (DOI: 10.1016/j.envint.2019.105256)</p>
Assembly and variant calling results of strain mixtures of HCMV
<p>The tgz files contain the assembly contigs of 10 different assemblers and variant calling results of 6 callers on the strain mixture dataset of the HCMV virus. </p>
Heteroplasmy Benchmark Dataset - mitochondrial DNA mixture model - MiSeq - U5-H1-M1-M2-M3-M4-M5 - FASTQ
<p>mtDNA mixture model of 2 mtDNA sequences belonging to haplogroups U5 and H1. Run on Illumina MiSeq with 3 different polymerases (Clontech, Herculase, NEB Taq), and different DNA extraction protocols - Paired-end Fastq files</p> <p>M1 = Mixture 1:2 i.e. 50%</p> <p>M2 = Mixture 1:10 i.e. 10%</p> <p>M3 = Mixture 1:50 i.e. 2%</p> <p>M4 = Mixture 1:100 i.e. 1%</p> <p>M5 = Mixture 1:200 i.e. 0.5%</p>
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
<p>1. In mixed stands, species complementarity (e.g., facilitation and competition reduction) may enhance forest tree productivity. Although positive mixture effects have been identified in forests worldwide, the majority of studies have focused on two-species interactions in managed systems with high functional diversity. We extended this line of research to examine mixture effects on tree productivity across landscape-scale compositional and environmental gradients in the low functional diversity, fire-suppressed, mixed-conifer forests of the U.S. Interior West.</p> <p>2. We investigated mixture effects on the productivity of <i>Pinus ponderosa</i>, <i>Pseudotsuga menziesii</i>, and <i>Abies concolor</i>. Using region-wide forest inventory data, we created individual-tree generalized linear mixed models and examined the growth of these species across community gradients. We compared the relative influences of stand structure, age, competition, and environmental stress on mixture effects using multi-model inference. We analyzed growth of neighboring tree species to infer whether a mixture effect in a single species translated to the stand-level.</p> <p>3. We found support for a positive mixture effect in <i>P. menziesii</i>, although our results were equivocal in light of a weaker but still plausible alternative model. Growth of <i>P. menziesii</i> neighboring species in mixed stands declined or held constant depending on aridity, suggesting that a positive mixture effect in <i>P. menziesii</i> does not necessarily extend to the stand level. We found no evidence for mixture effects in <i>P. ponderosa</i>, <i>A. concolor</i> or their neighboring species.</p> <p>4. Complementarity appears to have a limited influence on tree growth in the mixed-conifer systems of the U.S. Interior West, reflecting limited functional diversity. Historical changes in stand structure following fire exclusion, particularly high stand densities, may limit the potential for positive species mixture effects. The limited species pool of Interior West forests increases the risk that, without careful management, what functional diversity exists could be lost to compositional changes resulting from stand dynamics or disturbance.</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.