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104 results for “MOF”
Dataset of reports about MOF-based SERS substrates since 2011 until March 2023. Structure, characteristics, analytes, and performances.
<p>This dataset was generated to aid the creation of a review article addressing the use of Metal-Organic Frameworks (MOF)-based Surface Enhanced Raman Spectroscopy (SERS) platforms for the detection of Volatile Organic Compounds (VOCs).</p> <p>This dataset was generated employing the Web of Science database, encompassing manuscripts published up to March 2023. A literature search was initially conducted using a combination of keywords, including "MOF," "Metal-Organic Framework," "SERS," "Surface Enhanced Raman Spectroscopy," and "Surface Enhanced Raman Scattering." This search spanned the "Topic" category, enabling exploration across title, abstract, author keywords, and keyword-plus fields.</p> <p>From the initial pool of 238 documents, review articles and duplicates were systematically excluded, resulting in a refined collection of 182 articles. Subsequently, articles not concurrently addressing MOF and SERS or those utilizing MOF as sacrificial templates were further excluded, resulting in a final subset of 72 articles. From this curated set, relevant parameters were extracted, resulting in 229 entries for the dataset. </p> <p>Characteristics about the structure (in terms of MOF type and configuration; Plasmonic element type and configuration), target analyte (including type, phase, and incubation time), measurement specifications (in terms of laser, laser power, exposure time), and performance of the MOF-based SERS substrates were collected.</p> <p>Listed references 1-72 correspond with the manuscript number in the dataset.</p> <p>Listed references 73-80 correspond with references for selected examples of MOF pore diameters.</p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
Unveiling the atomistic and electronic structure of NiII–NO adduct in a MOF-based catalyst by EPR spectroscopy and quantum chemical modelling
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230706_01_CW_Xband </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_02_HYSCORE </strong>and <strong>PARACAT_WP4_20230706_03_ENDOR </strong>folders include X-band HYSCORE and ENDOR data; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_ 04_MATLAB</strong> and<strong> PARACAT_WP4_20230706_ 05_Modelling</strong> folders include matlab and computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> <li>File <strong>PARACAT_WP4_20230706_ 06_Origin</strong> include origin plotted data</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MFU– </strong>MFU-4l:NO<sub>2</sub> MOF material</li> <li>NiNO – NO adsorbed MFU-4l:NO<sub>2</sub> MOF</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
EPR and SQUID interrogations of Cr(III) trimer complexes in the MIL-101(Cr) and bimetallic MIL-100(Al/Cr) MOFs
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_08082023_01_MIL_CW </strong>folder includes X-, Q- and W-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_08082023_02_MIL_SQUID </strong>folder include SQUID magnetometry data; original data are in DAT file</li> <li>File <strong>PARACAT_WP4_08082023_02_MIL_Origin </strong>include origin plotted data</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul> <p> </p>
DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014
<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> </p> <p> </p>
Antibacterial Cu or Zn-MOFs Based on the 1,3,5-Tris-(styryl)benzene Tricarboxylate
<p>Metal–organic frameworks (MOFs) are highly versatile materials. Here, two novel MOFs, branded as IEF-23 and IEF-24 and based on an antibacterial tricarboxylate linker and zinc or copper cations, and holding antibacterial properties, are presented. The materials were synthesized by the solvothermal route and fully characterized. The antibacterial activity of IEF-23 and IEF-24 was investigated against Staphylococcus epidermidis and Escherichia coli via the agar diffusion method.<br>These bacteria are some of the most broadly propagated pathogens and are more prone to the development of antibacterial resistance. As such, they represent an archetype to evaluate the efficiency of novel antibacterial treatments. MOFs were active against both strains, exhibiting higher activity against Staphylococcus epidermidis. Thus, the potential of the developed MOFs as antibacterial agents was proved.</p>
Magnetic coupling of divalent metal centers in postsynthetic metal exchanged bimetallic DUT-49 MOFs by EPR spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_21_DUT49Mn@7K </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_00_DUT49Mn@simulation </strong>folder includes computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>DUT49Cu – </strong>DUT-49(Cu) MOF, <strong>DUT49Mn – </strong>DUT-49(Mn) MOF, <strong>DUT49CuZn – </strong>DUT-49(CuZn) MOF, <strong>DUT49MnCu – </strong>DUT-49(MnCu) MOF.</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> </ul> </li> </ul> <p>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</p>
MOSAEC MOF Database (MOSAEC-DB)
<p>A database of ~124,000 MOF crystal structures processed for atomistic simulations and machine learning. Novel solvent removal (SAMOSA) and error analysis (MOSAEC) tools were utilized to verify the chemical validity of the structures. Comprehensive details regarding the construction of this database are provided in the linked manuscript (https://doi.org/10.1039/D4SC07438F).</p> <p>All structures originate from experimental crystallographic information found in the Cambridge Structural Database (CSD). Access to the MOSAEC-DB structural data files (.cif) requires a valid CSD license, and they can now be downloaded through the CSD downloads page directly (https://www.ccdc.cam.ac.uk/support-and-resources/downloads/). This repository contains the remaining descriptors and information associated with each of the structural files.</p> <p>Use of MOSAEC-DB and its contents are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p> <p>Further information regarding the use of the MOSAEC-DB and the diverse subsets, including the use of the attached scripts, is provided in the README. </p> <p> </p>
Unconventional mechanical and thermal behaviours of MOF CALF-20
<p>Data for reproducibility of the paper:</p> <p><a href="https://scholar.google.com/citations?user=fq_Q0rkAAAAJ&hl=en%5D(https://scholar.google.com/citations?hl=en&user=fq_Q0rkAAAAJ&view_op=list_works&sortby=pubdate)" rel="nofollow">D. Fan</a>, <a href="https://scholar.google.com/citations?hl=en&user=7qyxfhAAAAAJ&view_op=list_works&sortby=pubdate" rel="nofollow">S. Naskar</a>, and <a href="https://scholar.google.com/citations?hl=en&user=QNfwyjgAAAAJ&view_op=list_works&sortby=pubdate" rel="nofollow">G. Maurin</a>, Unconventional mechanical and thermal behaviours of MOF CALF-20 D Fan, <em>Nature Communications</em>, <strong>15</strong>, 3251 (2024)</p> <p>https://doi.org/10.1038/s41467-024-47695-6 </p> <p>Please also refer to the main dataset:</p> <p><em><a href="https://doi.org/10.5281/zenodo.10815341" rel="nofollow">https://doi.org/10.5281/zenodo.10815341</a></em></p> <p>The citation of the datasets should be :</p> <p>Fan, D., Naskar, S., & Maurin G. Unconventional mechanical and thermal behaviours of MOF CALF-20. Zenodo database, https://doi.org/10.5281/zenodo.10815341, (2024)</p>
Thermal Expansion of MOFs
<p><span>This folder contains the raw LAMMPS simulation files for simulation of thermal expansion properties, which comprises 8,004 CoRE-MOFs and </span><span>42,089 ARC-MOFs used in our study.</span></p>
Dataset of the publication: Chemical design and magnetic ordering in thin layers of 2D MOFs
<p>Dataset of the publication: Chemical design and magnetic ordering in thin layers of 2D MOFs</p> <p>DOI: 10.1021/jacs.1c07802</p> <p>López-Cabrelles, J; Mañas-Valero, S; Vitórica-Yrezábal, IJ; Siskins, M; Lee, M; Steeneken, PG; van der Zant, HSJ; Espallargas, GM; Coronado, E<br>J. Am. Chem. Soc. 2021, 143, 44, 18502–18510</p>
Dataset of the publication: Tunable SIM properties in a family of 3D anilato-based Lanthanide-MOFs
<p>Dataset of the publication: Tunable SIM properties in a family of 3D anilato-based Lanthanide-MOFs</p> <p>DOI: 10.1039/d4qi01549e</p> <p>N. Monni, S. Dey, V. García-López, M. Oggianu, J. J. Baldoví, M. L. Mercuri, M. Clemente-León, E. Coronado</p> <p>Inorg. Chem. Front., 11, 5913-5923 (2024)</p>
Dataset of the publication: Reversible tuning of luminescence and magnetism in a structurally flexible erbium-anilato MOF
<p>Dataset of the publication: Reversible tuning of luminescence and magnetism in a structurally flexible erbium-anilato MOF</p> <p>DOI: 10.1039/d2sc00769j</p> <p>N. Monni, J. J. Baldoví, V. García-López, M. Oggianu, E. Cadoni, F. Quochi, M. Clemente-León, ML. Mercuri, E. Coronado</p> <p>Chem. Sci., 13, 25,7419-7428 (2022)</p>
Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs
<p>This dataset is associated with the study <em>"Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs."</em> Detailed instructions for installation, usage, and example scripts for the PACMOF2 models can be found on our GitHub <a href="https://github.com/snurr-group/pacmof2">repository</a>.</p> <div> <div> <div> <div> <p>The dataset includes the following files:</p> <ul> <li><strong>DDEC6_data.zip</strong>: Contains the crystal structures of MOFs with DDEC6 partial charges.</li> <li><strong>PACMOF2_prediction_cifs.zip</strong>: Contains the crystal structures of MOFs with DDEC6 charges as predicted by the PACMOF2 models.</li> <li><strong>PACMOF2_ionic.gz</strong>: A machine learning model designed to predict charges in ionic MOFs with a non-zero formal charge.</li> <li><strong>PACMOF2_neutral.gz</strong>: A machine learning model designed to predict charges in neutral MOFs.</li> </ul> <p>For more details about each file, please refer to the accompanying <code>README.md</code> file.<br><br><br>Updates: <br>- September 2024 (version 1.0.1): Added README.md file.</p> </div> </div> </div> </div>
Open Copper Site MOF (OCS-MOF) Database
<p>This dataset represents a curated collection of metal-organic frameworks (MOFs) with open copper sites (OCS) specifically selected for their potential in CO2/CH4 separation, which is essential for biogas upgrading. The OCS-MOFs were sourced and refined from the ARC-MOF database, following a comprehensive screening process to ensure novelty, validity, and applicability. The structures underwent rigorous quality control, including elimination of duplicates using pymatgen and MOFid, validation of physical and chemical integrity using MOFChecker, and assessment of pore accessibility via Zeo++.</p> <p>The dataset serves as the foundation for the multi-scale molecular simulations and machine learning models presented in our accompanying research. These models employ OCS-specialized force fields and diversity-transferable machine learning approaches to unveil structure-function relationships in MOFs, providing valuable insights into pore geometry and local chemical environments that optimize CO2 adsorption capacity and selectivity.</p> <p>For the accompanying machine learning pipline code, and tabulated data, please visit the GitHub repository here. https://github.com/xiaoyu961031/OCS-MOFs</p> <p>If you use this in your work, please cite our related publication: Xiaoyu Wu, Rui Zheng and Jianwen Jiang*. Leveraging Cross-Diversity Machine Learning to Unveil Metal-Organic Frameworks with Open Copper Sites for Biogas Upgrading. Journal of Chemical Theory and Computation, 2025. DOI:10.1021/acs.jctc.4c01478</p>
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models
<p>Dataset of MOFs constructed from building blocks of stable MOFs.</p> <p>Note: the columns labeled "rho" in features_and_properties are actually cell volume and not density.</p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014 DDEC Database
<p>~2,900 structures CoRE MOF 2014 structures with DDEC partial atomic charges. </p> <p> </p>
Nano MOFs as targeted drug delivery agents to combat antibiotic resistant bacterial infections
<p>The drug resistance of bacteria is a significant threat to human civilization while the action of antibiotics against drug-resistant bacteria is severely limited due to the hydrophobic nature of drug molecules, which unquestionably inhibit its permanency for clinical applications. The antibacterial action of nanomaterials offers major modalities to combat drug resistance of bacteria. The current work reports, the use of nano MOFs encapsulating drug molecules to enhance its antibacterial activity against model drug-resistant free living bacteria and biofilm of the bacteria. We have attached rifampicin (RF), a well-documented antituberculosis drug with tremendous pharmacological significance, into the pore surface of zeolitic imidazolate framework 8 (ZIF8) by a <span><span>simple synthetic procedure</span></span><span>.</span> The synthesized ZIF8 has been characterized using X-ray diffraction (XRD) method before and after drug encapsulation. The electron microscopic strategies such as scanning electron microscope (SEM) and transmission electron microscope (TEM) methods was performed to characterize the binding between ZIF8 and RF. We have also performed picosecond resolved fluorescence spectroscopy to validate the formation of the ZIF8-RF nanohybrids (NHs). The drug release profile experiment demonstrates that ZIF8-RF depicts pH-responsive drug delivery and ideal for targeting bacterial disease corresponding to its inherent acidic nature. Most remarkably, ZIF8-RF gives enhanced antibacterial activity against methicillin-resistant <i>S. aureus</i> (MRSA) bacteria and also prompts entire damage of structurally robust bacterial biofilms. Overall, the present study depicts a detailed physical insight for manufactured antibiotic-encapsulated NHs presenting tremendous antimicrobial activity that can be beneficial for manifold practical applications.</p>
Long-Term Stable Metal Organic Framework (MOF) Based Mixed Matrix Membranes for Ultrafiltration
<p><strong>Preprint available here:</strong> Al-shaeli, Muayad; Smith, Stefan J. D.; Jiang, Shanxue; Wang, Huanting; Zhang, Kaisong; Ladewig, Bradley P. (2020): Long-Term Stable Metal Organic Framework (MOF) Based Mixed Matrix Membranes for Ultrafiltration. ChemRxiv. Preprint. <a href="https://doi.org/10.26434/chemrxiv.13399367.v1">https://doi.org/10.26434/chemrxiv.13399367.v1</a></p> <p>Published manuscript: Muayad Al-Shaeli, Stefan J.D. Smith, Shanxue Jiang, Huanting Wang, Kaisong Zhang, Bradley P. Ladewig,<br> Long-term stable metal organic framework (MOF) based mixed matrix membranes for ultrafiltration,<br> Journal of Membrane Science, 2021, 119339, <a href="https://doi.org/10.1016/j.memsci.2021.119339">https://doi.org/10.1016/j.memsci.2021.119339</a></p> <p><strong>Abstract</strong></p> <p>In this study, novel mixed matrix membranes (MMMs) were synthesized by adding metal organic frameworks (MOFs) (UiO-66 and UiO-66-NH<sub>2</sub>) to pristine and sulfonated polyethersulfone (PES). The differing synthetic method resulting in MMM where additives were grafted to the matrix polymer, or formed a natural interface, allowing the impact of these MMM features to be investigated. The composite membranes were characterised by FTIR, PXRD, water contact angle, porosity, pore size, etc. Membrane performance was investigated by water permeation flux, flux recovery ratio, fouling resistance and anti-fouling performance. The stability test was also conducted for all the prepared mixed matrix membranes. A higher reduction in the water contact angle was observed after adding both MOFs to the PES and sulfonated PES membranes compared to pristine PES membranes. An enhancement in membrane performance was observed by embedding the MOFs into PES membrane matrix, with flux increased remarkably (565 LMH for PES+UiO-66-NH<sub>2</sub> at 5% loading and 487.1 LMH for SPES-UiO-66(10% binding) while the BSA rejection was still kept at a high level. By adding the MOFs into PES matrix, the flux recovery ratio was increased greatly (more than 99% for most mixed matrix membranes). The mixed matrix membranes showed higher resistance to protein adsorption compared to pristine PES membranes. After immersing the membranes in water for 3 months, 6 months and 12 months, both MOFs were stable and retained their structure. This study indicates that UiO-66 and UiO-66-NH<sub>2</sub> are great candidates for designing long-term stable mixed matrix membranes (MMMs) for applications in water and wastewater treatment.</p>
Unveiling MOF-808 Photocycle and its Interaction with Luminescent Guests
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.